JP5604574B1 - Object generation device, method, and program - Google Patents

Abstract

A user can interactively edit an object generated by an interface that allows easy input.
An interactive text analysis unit includes at least one word input to an output of an object model generated in response to an initial input that is at least one of text and speech including the input word. The editing operation represented by the dialog input is determined based on the dialog input that is text and voice and the initial input, and the generated object model is edited by the model editing unit 528 based on the determined editing operation.
[Selection] Figure 12

Description

  The present invention relates to an object generation device, method, and program, and more particularly, to an object generation device, method, and program for generating an object model.

  In the prior art, many text searches and image searches are performed by voice (Non-patent Document 11).

[online], Internet <URL: http: //www.ipa.go.jp/about/NYreport/201004.pdf>

  However, text search and image search outputs are completely different from real world outputs, such as being limited to image output in a computer.

  Moreover, it has not been proposed so far that an object is output from voice. This is a necessary technique for the user to output what he wants from a very easy interface.

  The present invention has been made in view of the above circumstances, and provides an object generation device, method, and program that allow a user to interactively edit an object generated using an interface that allows easy input. For the purpose.

  In order to achieve the above object, an object generating apparatus according to a first aspect of the present invention is input to an output of an object model generated corresponding to an initial input that is at least one of text and speech including an input word. In addition, a dialog text analysis unit that determines an editing operation represented by the dialog input based on the dialog input including text and sound including at least one word and the initial input, and the dialog text analysis unit A model editing unit that edits the generated object model based on an editing operation.

  An object generation method according to a second aspect of the present invention is an object generation method in an object generation apparatus including a dialog text analysis unit and a model editing unit, wherein the dialog text analysis unit includes text and speech including input words. The dialogue input based on the dialogue input which is text and speech including at least one word and is input to the output of the object model generated corresponding to the initial input which is at least one of the dialogue input The model editing unit edits the generated object model based on the editing operation determined by the dialog text analysis unit.

  According to a third aspect of the present invention, there is provided an object generation device that receives at least one input from an output of an object model generated corresponding to an initial input that is at least one of text and speech including two or more input words. A meaning between each of the at least one word of the dialogue input and each of the two or more words of the initial input based on the dialogue input that is text and speech including one word and the initial input A word selection unit that selects a word representing an editing object from the two or more words of the initial input, the at least one word of the dialog input, and the word selection unit Based on the selected word, a dialog text analysis unit for determining an editing operation represented by the dialog input, and the editing operation determined by the dialog text analysis unit. Based on, it is configured to include a, a model editing unit for editing the object model described above generated.

  An object generation method according to a fourth aspect of the present invention is an object generation method in an object generation apparatus including a word selection unit, a dialog text analysis unit, and a model editing unit, wherein the word selection unit includes two input Dialog input that is text and speech including at least one word, input to the output of the object model generated corresponding to the initial input that is at least one of text and speech including the word, and the initial input The two of the initial inputs using a semantic ease of connection between each of the at least one word of the dialog input and each of the two or more words of the initial input. From the above words, a word representing an edit target is selected, and the dialog text analysis unit selects the at least one word of the dialog input and the word selection. The editing operation represented by the dialog input is determined based on the word selected by the dialog input, and the model editing unit edits the generated object model based on the editing operation determined by the dialog text analysis unit. .

  The third invention further includes an operation candidate extraction unit that extracts at least one editing operation candidate from a plurality of predetermined editing operations based on the dialogue input, and the word selection unit includes the operation Using the co-occurrence relationship between words for each of the words representing the at least one editing operation candidate extracted by the candidate extraction unit and each of the two or more words of the initial input, the initial You may select the word showing the said edit object from the said two or more words of input.

  In the first and third inventions, an object model generated corresponding to the initial input may be a three-dimensional object model.

  In addition, the first and third inventions further include an initial dialog determining unit that determines whether at least one of text and speech including an input word is the initial input or the dialog input, and the dialog The text analysis unit may determine an editing operation represented by the dialog input when the initial dialog determination unit determines that the input is the dialog input.

  In addition, the first and third inventions may further include a three-dimensional modeling unit that forms a three-dimensional modeled object based on the object model edited by the model editing unit.

  A program according to a fifth invention is a program for causing a computer to function as each unit of the object generation device.

  According to the object generation device, method, and program of the present invention, there is an effect that a user can interactively edit an object generated by an interface that allows easy input.

It is the schematic which shows the structure of the three-dimensional object production | generation apparatus which concerns on the 1st Embodiment of this invention. It is a figure which shows the hierarchical structure of the object name of a 3D object model. It is a figure which shows a mode that the 3D object model after a deformation | transformation is output. It is a flowchart which shows the content of the three-dimensional object generation process routine in the three-dimensional object generation apparatus which concerns on the 1st Embodiment of this invention. It is the schematic which shows the structure of the three-dimensional object production | generation apparatus which concerns on the 2nd Embodiment of this invention. It is a figure which shows a mode that the acquired 3D object model is output. It is a figure which shows the formed three-dimensional structure. It is a flowchart which shows the content of the three-dimensional object generation process routine in the three-dimensional object generation apparatus which concerns on the 2nd Embodiment of this invention. It is the schematic which shows the structure of the three-dimensional object production | generation apparatus which concerns on the 3rd Embodiment of this invention. It is a flowchart which shows the content of the three-dimensional object production | generation routine in the three-dimensional object production | generation apparatus which concerns on the 3rd Embodiment of this invention. It is the schematic which shows the structure of the three-dimensional object production | generation apparatus which concerns on the 4th Embodiment of this invention. It is the schematic which shows the structure of the three-dimensional object production | generation apparatus which concerns on the 5th Embodiment of this invention. It is the schematic which shows the structure of the dialog text analysis part which concerns on the 5th Embodiment of this invention. It is a figure which shows the example of a word pair operation conversion table. It is a figure which shows the example of a keyword versus operation candidate correspondence table. It is a flowchart which shows the content of the three-dimensional object edit processing routine in the three-dimensional object production | generation apparatus which concerns on the 5th Embodiment of this invention. It is a flowchart which shows the content of the three-dimensional object edit processing routine in the three-dimensional object production | generation apparatus which concerns on the 5th Embodiment of this invention. It is the schematic which shows the structure of the three-dimensional object production | generation apparatus which concerns on the 6th Embodiment of this invention. It is the schematic which shows the structure of the dialog text analysis part which concerns on the 6th Embodiment of this invention. It is a flowchart which shows the content of the three-dimensional object edit processing routine in the three-dimensional object production | generation apparatus which concerns on the 6th Embodiment of this invention. It is a flowchart which shows the content of the three-dimensional object edit processing routine in the three-dimensional object production | generation apparatus which concerns on the 6th and 7th embodiment of this invention. It is the schematic which shows the structure of the three-dimensional object production | generation apparatus which concerns on the 7th Embodiment of this invention. It is the schematic which shows the structure of the dialog text analysis part which concerns on the 7th Embodiment of this invention. It is a flowchart which shows the content of the three-dimensional object edit process routine in the three-dimensional object production | generation apparatus which concerns on the 7th Embodiment of this invention. It is the schematic which shows the structure of the three-dimensional object production | generation apparatus which concerns on the 8th Embodiment of this invention. It is a flowchart which shows the content of the three-dimensional object edit process routine in the three-dimensional object production | generation apparatus which concerns on the 8th Embodiment of this invention. It is the schematic which shows the structure of the three-dimensional object production | generation apparatus which concerns on the 9th Embodiment of this invention. It is a figure which shows the example of a morphological analysis result. It is the schematic which shows the structure of the dialog text analysis part which concerns on the 9th Embodiment of this invention. It is a flowchart which shows the content of the three-dimensional object edit processing routine in the three-dimensional object production | generation apparatus which concerns on the 9th Embodiment of this invention. It is a figure which shows the three-dimensional structure which represents a character.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[First Embodiment]
<System configuration>
The three-dimensional object generation device 100 according to the first embodiment of the present invention performs processing for associating a 3D object model from the input text and further transforming it. The three-dimensional object generation device 100 is composed of a computer including a CPU, a RAM, and a ROM that stores a program for executing a three-dimensional object generation processing routine to be described later. It is configured. As illustrated in FIG. 1, the three-dimensional object generation device 100 includes an input unit 10, a calculation unit 20, and a model output unit 30.

  The input unit 10 receives text input related to the 3D object model using a keyboard or the like. For example, a text input “10 cm yellow car” is accepted.

  The calculation unit 20 includes a text analysis unit 21, a 3D object model database 22, a model acquisition unit 23, and a model deformation unit 24.

  The text analysis unit 21 identifies a text expression (for example, “car”) related to the target 3D object model from the input text.

  For example, simple matching between the object name of the 3D object model registered in the 3D object model database 22 and the input text is performed to identify the text expression related to the target 3D object model. Alternatively, the morphological analysis is performed on the input text, and based on the result of the morphological analysis, the part of speech is a noun or the specific expression about the object is extracted and identified as the text expression regarding the target 3D object model. .

  Further, the text analysis unit 21 identifies attribute values (for example, “10 cm”, “yellow”, “5”, etc.) to be added to the text expression related to the target 3D object model from the input text. The attribute value is not particularly limited as long as it relates to the object, such as color, size, and number.

  The attribute value is identified by using a regular expression (for example, “/.+c?m/,/.+pieces/”) for identifying the attribute value from text. May be. In addition, when a text expression related to a 3D object model is identified by extraction of a specific expression, and the length and color are defined in advance in the specific expression class (for example, extended specific expression definition of Sekine), The length and color defined in the extracted specific expression class may be identified as the attribute value. In addition, when text expressions related to a plurality of objects are input (for example, “red car and green house”), dependency analysis is performed on the text, and the result of dependency analysis is used to generate a 3D object. An attribute value may be identified for each text expression related to the model (for example, “car” ← red, “house” ← green).

  The 3D object model database 22 stores a plurality of 3D object model data in association with object names of the 3D object model. Here, the 3D object model is a virtual 3D model on a computer or the like. In the 3D object model database 22, data of a plurality of 3D object models are stored according to a predefined hierarchical structure. The object name may be other than a noun or proper noun indicating the object, or may be an arbitrary label.

  The model acquisition unit 23 associates the text representation related to the 3D object model identified by the text analysis unit 21 with the object name stored in the 3D object model database 22, and converts the 3D object model data of the object name to 3D Obtained from the object model database 22 (“car” → car object with a size of 3 m and a red color).

  Here, the association between the identified text representation and the object name is performed by solving as a hierarchical classification problem in which the object name is regarded as a class label. The hierarchy definition is assumed to be predetermined. As for the classification model, it is only necessary to prepare a plurality of learning data in which a text expression and a class label (object name) are associated with each other on a one-to-one basis, and learn a classification model for each layer based on the learning data. .

  FIG. 2 shows a conceptual diagram. For example, each of the text expressions “car”, “four wheels”, and “blue” is classified as “vehicle object” in FIG. 2 and then classified as “car object”. It is tied to.

  As a result of the classification process, when it is determined that the 3D object model as the corresponding leaf object model does not exist because the leaf object model is not reached, the object is classified into the object of the next higher level.

  For example, when a 3D object as a leaf object model corresponding to “Soba” does not exist, it is classified into a higher class “noodle dish” object.

  The model deforming unit 24 deforms the 3D object model obtained by the model acquiring unit 23 according to the attribute value identified by the text analyzing unit 21, and, as shown in FIG. The model output unit 30 outputs a yellow car object model having a length of 10 cm. Here, the post-deformation 3D object model is a virtual 3D model on a computer or the like that has been deformed according to the content of the text.

  For example, when the attribute value identified by the text analysis unit 21 is an attribute value related to size or color, the attribute value related to size or color is described in VRML (Virtual Reality Modeling Language) format. To deform the 3D object. If the attribute value identified by the text analysis unit 21 is an attribute value related to a pattern (texture), the 3D object is deformed by editing the texture imagewise. If the attribute value identified by the text analysis unit 21 is an attribute value related to the number, the 3D object is deformed from a plurality of objects using the copy and paste function of the CAD editing software.

<Operation of three-dimensional object generation device>
Next, the operation of the three-dimensional object generation device 100 according to the first embodiment will be described. First, when text including a text representation and attribute values relating to a 3D object model to be generated is input to the 3D object generation device 100, the 3D object generation device 100 executes a 3D object generation processing routine shown in FIG. Is done.

  First, in step S101, the input text is analyzed to identify the text representation and attribute values related to the 3D object model.

  In step S102, the text representation related to the 3D object model identified in step S101 is classified into one of the object names of the 3D object model in the 3D object model database 22 according to the classification model and hierarchical structure prepared in advance, and classified. Data of the 3D object model corresponding to the object name is acquired from the 3D object model database 22.

  In the next step S103, the data of the 3D object model acquired in step S102 is transformed according to the attribute value identified in step S101. In step S104, the transformed 3D object model obtained in step S103 is output by the model output unit 30, and the three-dimensional object generation processing routine is terminated.

  As described above, according to the three-dimensional object generation device according to the first embodiment, the text input by the keyboard or the like is analyzed and the three-dimensional object is virtually output as a real object. A three-dimensional object can be obtained from a very easy interface. Further, the three-dimensional object can be deformed by the input text.

  In the above-described embodiment, the description has been given of the case where the text expression related to the 3D object model is classified into the object name according to the hierarchical structure, but the present invention is not limited to this. The text representation related to the 3D object model may be classified into object names classified into one hierarchy.

  Moreover, you may make it form the three-dimensional modeling thing showing a 3D object model after a deformation | transformation by a three-dimensional modeling part similarly to 2nd Embodiment mentioned later.

  Further, as in a second embodiment to be described later, a voice input may be received and a text input by voice recognition may be analyzed.

[Second Embodiment]
<System configuration>
Next, a three-dimensional object generation device according to the second embodiment will be described. In addition, about the part which becomes the structure similar to 1st Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The second embodiment is different from the first embodiment in that a voice input is received and a three-dimensional structure is formed based on the post-deformation 3D object model. .

  As illustrated in FIG. 5, the three-dimensional object generation device 200 according to the second embodiment includes an input unit 10, a calculation unit 220, a model output unit 30, and a three-dimensional modeling unit 230.

  The input unit 10 receives a voice input related to the 3D object model. For example, a voice input “10 cm yellow car” is accepted.

  The three-dimensional modeling unit 230 forms a three-dimensional modeled object according to control by the calculation unit 220. As the three-dimensional modeling unit 230, a conventionally known three-dimensional printer may be used, and detailed description thereof is omitted. The output method is not particularly limited as long as it can form a three-dimensional object in the real world.

  The calculation unit 220 includes a speech recognition unit 221, a text analysis unit 21, a 3D object model database 22, a model acquisition unit 23, a model deformation unit 24, and a 3D modeling control unit 225.

  The voice recognition unit 221 performs voice recognition processing on the input voice and converts it into text (for example, “10 cm yellow car”). Note that the content of the speech recognition process may be conventional speech recognition (for example, speech recognition using a hidden Markov model or HMM), and any method may be used.

  The text analysis unit 21 identifies a text expression (for example, “car”) and attribute values (for example, “10 cm”, “yellow”) regarding the target 3D object model from the text converted by the speech recognition unit 221. To do.

  The model acquisition unit 23 associates the text representation related to the 3D object model identified by the text analysis unit 21 with the object name stored in the 3D object model database 22, and converts the 3D object model data of the object name to 3D Obtained from the object model database 22 and the obtained 3D object model is output by the model output unit 30 as shown in FIG.

  The model deformation unit 24 deforms the 3D object model obtained by the model acquisition unit 23 according to the attribute value identified by the text analysis unit 21, and outputs the deformed 3D object model as a model output as shown in FIG. Output by the unit 30. The model deforming unit 24 intentionally divides the post-deformation 3D object model in order to speed up the subsequent three-dimensional modeling. For example, the axial direction in which the size of the post-deformation 3D object model is the shortest is obtained from an arbitrary axial direction in the three-dimensional space, and the post-deformation 3D object model is divided into N in the obtained axial direction. At this time, the division is performed so that the number of objects does not become larger than double. In addition, marking is performed on the divided objects so that the adhesion surface is uniquely determined.

  The three-dimensional modeling control unit 225 controls the three-dimensional modeling unit 230 such that a three-dimensional modeled object (see FIG. 7) representing the post-deformation 3D object model obtained by the model deforming unit 24 is formed. At this time, the three-dimensional modeling unit 230 is controlled so that a three-dimensional model is formed for each divided object.

<Operation of three-dimensional object generation device>
Next, the operation of the three-dimensional object generation device 200 according to the second embodiment will be described. First, when a voice representing a text representation and an attribute value related to a 3D object model to be generated is input to the 3D object generation device 200, the 3D object generation processing routine shown in FIG. Is done. In addition, about the process similar to 1st Embodiment, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

  First, in step S201, speech recognition processing is performed on the input speech to convert the speech into text. In step S101, the text obtained in step S201 is analyzed to identify text representations and attribute values related to the 3D object model.

  In step S102, the text representation related to the 3D object model identified in step S101 is classified into any of the object names of the 3D object model in the 3D object model database 22, and the 3D object model corresponding to the classified object name is classified. Are obtained from the 3D object model database 22 and output by the model output unit 30.

  In the next step S103, the data of the 3D object model acquired in step S102 is transformed according to the attribute value identified in step S101 and output by the model output unit 30.

  In step S204, the three-dimensional modeling unit 230 is controlled to finish the three-dimensional object generation processing routine so that the three-dimensional model representing the post-deformation 3D object model obtained in step S103 is formed. To do.

  As described above, according to the three-dimensional object generation device according to the second embodiment, in order to analyze a text input by speech recognition and form a three-dimensional structure in real space as a real object, A user can obtain a three-dimensional structure from an extremely easy interface. In addition, a three-dimensional object can be transformed by text input by voice recognition.

  Further, the post-deformation 3D object model is divided N times in the axial direction in which the size of the post-deformation 3D object model is the shortest, so that the number of objects does not become larger than twice due to the division. The 3D object model can be divided into minimum parts.

[Third Embodiment]
<System configuration>
Next, a three-dimensional object generation device according to a third embodiment will be described. In addition, about the part which becomes the same structure as 1st Embodiment and 2nd Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The third embodiment is different from the second embodiment in that a voice input is accepted in a situation where a 3D object model already exists.

  As illustrated in FIG. 9, the three-dimensional object generation device 300 according to the third embodiment includes an input unit 10, a calculation unit 320, a model output unit 30, and a three-dimensional modeling unit 230.

  The input unit 10 receives an input of 3D object model data and also receives an audio input related to an attribute value of the 3D object model. For example, an input of data of a 3D object model of a car is accepted, and a voice input “turn the car yellow” is accepted. The model output unit 30 outputs the input 3D object model.

  The calculation unit 220 includes a voice recognition unit 221, a text analysis unit 321, a model deformation unit 324, and a three-dimensional modeling control unit 225.

  The text analysis unit 321 identifies attribute values related to the target 3D object model from the text converted by the speech recognition unit 221.

  The model deforming unit 324 deforms the input 3D object model according to the attribute value identified by the text analyzing unit 321, and outputs the deformed 3D object model by the model output unit 30.

<Operation of three-dimensional object generation device>
Next, the operation of the three-dimensional object generation device 300 according to the third embodiment of the present invention will be described. First, when 3D object model data is input to the three-dimensional object generation device 300, the input 3D object model is output by the model output unit 30. Then, when sound representing attribute values related to the 3D object model is input to the 3D object generation device 300, the 3D object generation processing routine shown in FIG. In addition, about the process similar to 2nd Embodiment, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

  First, in step S201, speech recognition processing is performed on the input speech to convert the speech into text. In step S301, the text obtained in step S201 is analyzed to identify attribute values related to the 3D object model.

  In step S302, the input 3D object model data is transformed according to the attribute value identified in step S301, and is output by the model output unit 30.

  In step S204, the three-dimensional modeling unit 230 is controlled to finish the three-dimensional object generation processing routine so that a three-dimensional model representing the post-deformation 3D object model obtained in step S302 is formed. To do.

  As described above, according to the three-dimensional object generation device according to the third embodiment, the text input by speech recognition is analyzed, the three-dimensional object is deformed, and the three-dimensional object in the real space is analyzed. In order to form a three-dimensional structure, the user can obtain a three-dimensional structure from an extremely easy interface.

  In the above embodiment, the case where voice input is accepted has been described as an example. However, the present invention is not limited to this, and text input may be accepted.

  Further, the post-deformation 3D object model may be simply output by the model output unit without forming the three-dimensional structure representing the post-deformation 3D object model.

[Fourth Embodiment]
<System configuration>
Next, a three-dimensional object generation device according to a fourth embodiment will be described. In addition, about the part which becomes the same structure as 1st Embodiment and 2nd Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The fourth embodiment is different from the second embodiment in that it is classified into the object name of the 3D object model with the voice input.

  As illustrated in FIG. 11, the three-dimensional object generation device 400 according to the fourth embodiment includes an input unit 10, a calculation unit 420, a model output unit 30, and a three-dimensional modeling unit 230.

  The input unit 10 receives a voice input related to the 3D object model.

  The calculation unit 420 includes a voice analysis unit 421, a 3D object model database 22, a model acquisition unit 423, a model deformation unit 24, and a three-dimensional modeling control unit 225.

  The voice analysis unit 421 identifies the object name and attribute value related to the target 3D object model from the input voice.

  Specifically, the input audio signal is classified into object names stored in the 3D object model database 22. As for the classification model, a plurality of learning data in which audio signals and class labels (object names) are associated one-to-one are prepared, and the classification model is learned based on the learning data.

  Also, the input audio signal is classified into attribute values related to the 3D object model. As for the classification model, a plurality of learning data in which a speech signal and a class label (attribute value) are associated one-to-one are prepared, and the classification model is learned based on the learning data.

  The model acquisition unit 423 acquires the 3D object model data of the object name identified by the voice analysis unit 421 from the 3D object model database 22, and the model output unit 30 outputs the acquired 3D object model.

  The model deformation unit 24 deforms the 3D object model obtained by the model acquisition unit 423 according to the attribute value identified by the speech analysis unit 421, and outputs the deformed 3D object model by the model output unit 30.

  Note that the other configurations and operations of the three-dimensional object generation device according to the fourth embodiment are the same as those of the second embodiment, and thus the description thereof is omitted.

  In this way, since the voice input is analyzed and the three-dimensional structure is formed in the real space as a real object, the user can obtain the three-dimensional structure from a very easy interface.

[Fifth Embodiment]
<System configuration>
Next, a three-dimensional object generation device according to a fifth embodiment will be described. In addition, about the part which becomes the structure and effect | action similar to 1st Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  In the first to fourth embodiments described above, it is possible to output a three-dimensional object by text input or voice. However, the output depends on the content of one utterance and does not correspond to further editing the output.

  The fifth embodiment is different from the first embodiment in that the 3D object model generated based on the initial text is edited based on the dialogue text.

  As illustrated in FIG. 12, the three-dimensional object generation device 500 according to the fifth embodiment includes an input unit 10, a calculation unit 520, and a model output unit 30.

  The input unit 10 receives a text input of an initial text related to the 3D object model using a keyboard or the like. For example, an initial text input of “10 cm yellow car” is accepted. The input unit 10 also accepts text input of interactive text related to editing operations. For example, a text input of a dialog text “more thinly” is accepted. In addition, the input unit 10 receives a text input of an end text associated with the end process. For example, a text input “end” is accepted. Here, the initial text is text intended to instruct the 3D object model and its attribute value to the 3D object generation device 500 first, and the dialog text is obtained based on the analysis result of the initial text already obtained. This text is intended to be interactively edited with the 3D object generation apparatus 500 for the 3D object model that has been selected. Note that the initial text is given only once at the first input, and all the text after outputting the 3D object model is the dialog text or the end text. The initial text is an example of initial input, and the dialog text is an example of dialog input. A 3D object model is an example of an object model.

  The calculation unit 20 includes a 3D object model database 22, a model acquisition unit 23, an initial text analysis unit 521, a model deformation unit 524, a dialog text analysis unit 526, and a model editing unit 528.

  The initial text analysis unit 521 identifies a text representation (for example, “car”) related to the target 3D object model from the input initial text, and converts the text representation related to the target 3D object model from the input text. The attribute value to be added (for example, “10 cm”, “yellow”, “5”, etc.) is identified.

  When the text analysis unit 21 identifies the attribute value, the model transformation unit 524 transforms the 3D object model obtained by the model acquisition unit 23 according to the attribute value identified by the text analysis unit 21 and is shown in FIG. As described above, the deformed 3D object model is stored in a memory (not shown) and is output by the model output unit 30.

  When the text analysis unit 21 does not identify the attribute value, the model transformation unit 524 stores the 3D object model obtained by the model acquisition unit 23 in a memory (not shown) and outputs it from the model output unit 30. .

  As shown in FIG. 13, the dialog text analysis unit 526 includes an end determination unit 550, a word extraction unit 552, an operation candidate extraction unit 554, a word selection unit 556, an operation ranking unit 557, a connection unit 558, and a change amount calculation unit 560. , A word pair operation conversion database 570, a natural sentence set database 572, a keyword pair operation candidate correspondence database 574, and a conjunction particle database 576. Further, the dialog text analysis unit 526 identifies the editing content based on the input text relating to the editing operation, and outputs it to the model editing unit 528. In addition, based on the text associated with the end process, a notification to end the process is output to the model editing unit 528.

  The model editing unit 528 reads the 3D object model stored in the memory, edits it according to the editing operation identified by the dialog text analysis unit 526, stores the edited 3D object model in the memory (not shown), Output by the model output unit 30. The editing of the 3D object model may be performed in the same manner as the deformation of the 3D object model by the model deformation unit 524.

  Next, the configuration of the dialog text analysis unit 526 shown in FIG. 13 will be described in detail.

  The end determination unit 550 determines whether or not the input text is an end text associated with the end process. Specifically, it is determined whether or not the input text matches a keyword associated with a predetermined end process. If they match, it is determined that the input text is the end text, otherwise Is determined to be dialogue text. When it is determined that the end text has been input, a process end notification is output to the model editing unit 528. On the other hand, when it is determined that the dialogue text has been input, the process proceeds to the processing of the word extraction unit 552.

  When the text input by the end determination unit 550 is determined to be a dialog text, the word extraction unit 552 performs a morphological analysis on the input dialog text, and uses a verb (for example, more) and a word (for example, a word representing an editing operation). , Thin) each. Specifically, each of the word representing the conjunction and the editing operation is extracted using general sequence labeling (HMM or CRF). In addition, you may make it extract each of the word which represents a conjunction and a editing operation by a rule base.

  The operation candidate extraction unit 554 is input based on the word representing the editing operation extracted by the word extraction unit 552 and the word pair operation conversion table as shown in FIG. 14 stored in the word pair operation conversion database 570. The editing operation candidate represented by the dialogue text is extracted. Specifically, for example, when the word “light” representing the editing operation extracted by the word extraction unit 552 is extracted, the editing operation candidate “associated with the word“ light ”for the word-to-operation conversion database“ “Color operation” and “shape operation (thickness change)” are extracted as editing operation candidates. When one editing operation candidate is extracted as an editing operation candidate, the editing operation candidate is identified as an editing operation.

  When a plurality of words are included in the analysis result of the initial text input from the initial text analysis unit 521, the word selection unit 556 determines the analysis result of the initial text input from the initial text analysis unit 521 and the word extraction unit 552. Select a word to be edited from words included in the initial text, based on the extracted word representing the editing operation candidate and a predetermined natural sentence document set stored in the natural sentence set database 572 To do. If there is one editing operation candidate extracted by the operation candidate extraction unit 554, processing in the word selection unit 556 is not performed.

Specifically, when the analysis result of the initial text input from the initial text analysis unit 521 includes a plurality of words (when the initial text includes a plurality of words), each of the words in the initial text, and word extraction The co-occurrence relationship between words in the natural sentence document set stored in the natural sentence set database 572 (the number of sentences in which two words co-occur) with respect to the word representing the editing operation extracted in the section 552 ) To select a word to be edited from words in the initial text. Specifically, when there are a plurality of words “car” and “red” in the initial text, the word “car” in the initial text and the word “representing the editing operation extracted by the word extraction unit 552”. Search for sentences in which the word pair co-occurs in the document set stored in the natural sentence set database 572 using the word pair “thin”, and calculate the number of sentences included in the search results; Using the word pair of the word “red” in the initial text and the word “light” representing the editing operation extracted by the word extraction unit 552, the word pair in the document set stored in the natural sentence set database 572 Is searched, and the number of sentences included in the search result is calculated. Then, the calculated number of sentences is compared, and a word in the initial text included in the word pair having the larger number of co-occurring sentences is selected as a word to be edited. In the present embodiment, for example, the co-occurrence frequency of the word pair of the word “car” and the word “thin” is once, and the co-occurrence frequency of the word pair of the word “red” and the word “thin” is 100 times. Select the word “red”.
In addition, when only one word is included in the analysis result of the initial text, the word selection unit 556 selects the word as a word to be edited.

  The operation ranking unit 557 includes a word selected by the word selection unit 556, two or more editing operation candidates extracted by the operation candidate extraction unit 554, and a word representing the editing operation extracted by the word extraction unit 552. The editing operation candidates are ranked based on the keyword-to-operation candidate correspondence table stored in the keyword-to-operation candidate correspondence database 574. Note that when there is one editing operation candidate extracted by the operation candidate extraction unit 554, processing in the operation ranking unit 556 is not performed.

  Specifically, based on the pair of the word selected by the word selection unit 556 and the word representing the edit target extracted by the word extraction unit 552, the keyword pair stored in the keyword pair operation candidate correspondence database 574 is stored. Using the operation candidate correspondence table, ranking is performed as to whether or not each of the editing operation candidates extracted by the operation candidate extraction unit 554 is appropriate as an editing operation. Specifically, a pair of the word “red” selected by the word selection unit 556 and the word “light” representing the editing operation extracted by the word extraction unit 552 (or only the word “red”) is an operation candidate extraction unit. Which of the editing operation candidates “color operation” and “shape operation” extracted in 554 is deeply related is ranked using a keyword-to-operation candidate correspondence table as shown in FIG. In the example of the keyword pair operation candidate correspondence table in FIG. 15, the degree that the color operation is related to the editing operation for the pair of the word “red” and the word “light” representing the editing operation is 0.9, Since the degree that the operation is related to the editing operation is 0.1, the pair is deeply related to the color operation, and the operation candidate “color operation” extracted by the operation candidate extraction unit 554 is ranked first, “shape operation” "Is ranked second, and the first editing operation candidate" color operation "is identified as the editing operation.

  The change amount calculation unit 560 calculates the change amount from the word representing the conjunctions extracted by the word extraction unit 552. Specifically, for example, when the word “more” is extracted in the word extraction unit 552, the amount of change corresponding to the conjunction “more” is extracted from the conjunction particle change amount table stored in the conjunction particle database 576. To do. For example, when “more” → double and “more” → 1.5 times are defined in the conjunction particle change amount table, the variation corresponding to the conjunction “more” is doubled.

  The connecting unit 558 connects the editing operation identified as the editing operation by the operation candidate extracting unit 554 or the operation ranking unit 557 and the change amount calculated by the change amount calculating unit 560, and performs an edit operation including the change amount. Identify and output to the model editing unit 528. Specifically, for example, when the editing operation is a “color operation” and the change amount calculation unit 560 calculates “double”, the editing operation including the deformation amount is “double the color saturation”. Is identified.

  The word pair operation conversion database 570 stores a word pair operation conversion table as shown in FIG. 14 in which editing operation candidates having a relationship with a specific word are defined. The word pair operation conversion table may be constructed manually or may be automatically obtained from learning data or the like.

  The natural sentence set database 572 stores a set of documents including a plurality of natural sentences.

  The keyword-to-operation candidate correspondence database 574 stores a keyword-to-operation candidate correspondence table that defines editing operation candidates having a relationship with a specific word pair. The keyword-to-operation candidate correspondence table may be constructed manually or may be automatically obtained from learning data or the like.

  The conjunction particle database 576 stores a conjunction particle change amount table defining specific conjunctions and change amounts. Note that the continuum change amount table may be constructed manually or automatically obtained from learning data or the like.

<Operation of three-dimensional object generation device>
Next, the operation of the three-dimensional object generation device 500 according to the fifth embodiment will be described. First, when initial text relating to a 3D object model to be generated is input to the three-dimensional object generation device 500, the three-dimensional object generation device 500 executes a three-dimensional object editing processing routine shown in FIGS.

  First, in step S501, the input initial text is analyzed to identify the text representation and attribute values related to the 3D object model.

  In step S102, the text representation related to the 3D object model identified in step S501 is classified into any of the object names of the 3D object model in the 3D object model database 22 according to the classification model and hierarchical structure prepared in advance, and classified. Data of the 3D object model corresponding to the object name is acquired from the 3D object model database 22.

  In the next step S103, the data of the 3D object model acquired in step S102 is transformed according to the attribute value identified in step S501. In step S <b> 104, the model output unit 30 outputs the transformed 3D object model obtained in step S <b> 103. If no attribute value is identified in step S101, the model output unit 30 outputs the 3D object model acquired in step S102.

  Next, in step S502, the continuous verb change amount table stored in the continuous verb database 576 is read.

  In step S504, the word pair operation conversion table stored in the word pair operation conversion database 570 is read.

  In step S508, the keyword / operation candidate correspondence table stored in the keyword / operation candidate correspondence database 574 is read.

  Next, in step S510, text input to the modified 3D object model or 3D object model output in step S104 or the edited 3D object model output in step S528 described later is received. Note that the process waits in step S510 until the text is accepted.

  Next, in step S512, it is determined whether or not the text received in step S510 is an end text. If the accepted text is an end text, the process is terminated. On the other hand, if the accepted text is not an end text, it is determined that the accepted text is a dialog text, and the process proceeds to step S514.

  Next, in step S514, a morphological analysis is performed on the dialog text input in step S510, and each of a conjunction (for example, more) and a word (for example, light) representing an editing operation are extracted.

  Next, in step S516, based on the word representing the editing operation extracted in step S514 and the word-to-operation conversion table read in step S504, editing operation candidates represented by the input dialogue text are extracted. If there is one extracted editing operation candidate, the editing operation candidate is identified as an editing operation.

  Next, in step S518, based on the analysis result of the initial text analyzed in step S501, the word representing the editing operation extracted in step S514, and the natural sentence document set stored in the natural sentence set database 572. The word to be edited is selected from the words included in the initial text. If there is one editing operation candidate extracted in step S516, the process in step S518 is not performed.

  Next, in step S520, the word selected in step S518, each of the editing operation candidates extracted in step S516, the word representing the editing operation extracted in step S514, and the keyword-to-operation candidate correspondence table read in step S508 Based on the above, the editing operation candidates are ranked. If there is one editing operation candidate extracted in step S516, the processing in step S520 is not performed. Further, the editing operation candidate that is ranked first is identified as an editing operation.

  Next, in step S522, the amount of change is calculated based on the conjunctions extracted in step S514 and the conjunctions change amount table read in step S502.

  Next, in step S524, the editing operation identified in step S516 or step S520 and the change amount calculated in step S522 are connected to identify an editing operation including the change amount.

  Next, in step S526, the modified 3D object model or 3D object model obtained in step S103 or the edited 3D object model obtained in step S526 of the previous process is edited according to the editing operation identified in step S524. To do.

  Next, in step S528, the edited 3D object model edited in step S526 is output by the model output unit 30, and the process proceeds to step S510.

  As described above, according to the three-dimensional object generation device according to the fifth embodiment, the initial text input with the keyboard or the like is analyzed, and the three-dimensional object is virtually output as a real object. A three-dimensional object can be obtained from a very easy interface. Further, the three-dimensional object can be deformed by the input initial text. Furthermore, the initial text or the virtual three-dimensional object obtained by the dialog text can be edited by the input dialog text.

  Further, it is possible to identify the editing operation for the three-dimensional object after identifying / ranking which editing operation is associated with the word to be edited from the dialogue text.

  In the above-described embodiment, the description has been given of the case where the text expression related to the 3D object model is classified into the object name according to the hierarchical structure, but the present invention is not limited to this. The text representation related to the 3D object model may be classified into object names classified into one hierarchy.

  Moreover, you may make it form the three-dimensional modeling thing showing a 3D object model after editing by a three-dimensional modeling part similarly to 6th Embodiment mentioned later.

  Further, as in a sixth embodiment to be described later, a voice input may be received and a text input by voice recognition may be analyzed.

  In addition, the dialog text may be input as a one-way intermittent utterance from the user side, or may be input in the middle of some reaction from the system side.

  Further, by using a word pair of a word in the initial text and a word representing an editing operation extracted by the word extraction unit 552, a sentence in which the word pair co-occurs in a natural sentence document set is searched, Although the case of calculating the number of sentences in which word pairs co-occur has been described, this is not restrictive. For example, for each of a specific word in the initial text and a word pair representing a specific editing operation, the number of sentences in which the word pair co-occurs is obtained in advance, and the word in the initial text and the word extraction unit The number of sentences in which word pairs with the word representing the editing operation extracted in 552 co-occur may be acquired.

  Similarly to a ninth embodiment to be described later, an input text may be accepted to determine whether the text is an initial text or a dialog text.

[Sixth Embodiment]
<System configuration>
Next, a three-dimensional object generation device according to a sixth embodiment will be described. In addition, about the part which becomes the structure and effect | action similar to 1st-5th Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The sixth embodiment is different from the fifth embodiment in that a voice input is accepted and a three-dimensional structure is formed based on the edited 3D object model. .

  As illustrated in FIG. 18, the three-dimensional object generation device 600 according to the sixth embodiment includes an input unit 10, a calculation unit 620, a model output unit 30, and a three-dimensional modeling unit 230.

  The input unit 10 receives a voice input representing initial text related to the 3D object model. For example, a voice input representing an initial text “10 cm yellow car” is accepted. In addition, the input unit 10 receives a voice input representing a dialog text related to an editing operation. For example, a voice input representing a dialog text “more thinly” is accepted. In addition, the input unit 10 receives a voice input representing an end text related to the end process. For example, a voice input representing an end text “end” is received. Note that the audio input input for the first time (before the output of the 3D object model) is an audio input representing the initial text related to the 3D object model, and the audio input input after outputting the 3D object model is related to the editing operation. It is assumed that the voice input represents a dialog text or the voice input representing the end text related to the end process.

  The calculation unit 220 includes a voice recognition unit 221, an initial text analysis unit 521, a 3D object model database 22, a model acquisition unit 23, a model transformation unit 524, a 3D modeling control unit 225, a dialog text analysis unit 626, and a model editing unit 528. It has.

  The voice recognition unit 221 performs voice recognition processing on the input voice and converts it into text (for example, “10 cm yellow car”). Note that the content of the speech recognition process may be conventional speech recognition (for example, speech recognition using a hidden Markov model or HMM), and any method may be used. Note that the speech recognition result for the speech input representing the initial text related to the 3D object model is output to the initial text analysis unit 521, and the speech recognition result for the speech input representing the dialogue text or the termination text related to the editing operation or the termination process It is output to the dialog text analysis unit 626.

  The model editing unit 528 reads out the 3D object model, the transformed 3D object model, or the edited 3D object model stored in the memory, and edits the edited 3D object model according to the editing operation identified by the dialog text analysis unit 526. The object model is stored in a memory (not shown) and output by the model output unit 30. Further, the model editing unit 528 intentionally divides the edited 3D object model in order to speed up the subsequent three-dimensional modeling. For example, the axial direction in which the size of the edited 3D object model is the shortest is obtained from an arbitrary axial direction in the three-dimensional space, and the edited 3D object model is divided into N in the obtained axial direction. At this time, the division is performed so that the number of objects does not become larger than double. In addition, marking is performed on the divided objects so that the adhesion surface is uniquely determined. When a notification to end the process is received from the dialog text analysis unit 526, the 3D object model, the modified 3D object model, or the edited 3D object model that is finally stored in the memory is converted into the three-dimensional modeling. Output to the control unit 225.

  The three-dimensional modeling control unit 225 performs the three-dimensional modeling so that a three-dimensional model representing the 3D object model, the transformed 3D object model, or the edited 3D object model input from the model editing unit 528 is formed. The unit 230 is controlled. At this time, the three-dimensional modeling unit 230 is controlled so that a three-dimensional model is formed for each divided object.

  As shown in FIG. 19, the dialog text analysis unit 626 includes an end determination unit 550, a word extraction unit 552, an operation candidate extraction unit 554, a word selection unit 556, an operation ranking unit 557, a connection unit 558, and a change amount calculation unit 560. , A word pair operation conversion database 570, a natural sentence set database 572, a keyword pair operation candidate correspondence database 574, and a conjunction particle database 576.

<Operation of three-dimensional object generation device>
Next, the operation of the three-dimensional object generation device 600 according to the sixth embodiment will be described. First, when speech representing an initial text including a text representation and an attribute value regarding a 3D object model to be generated is input to the 3D object generation device 600, the 3D object generation device 600 causes the 3D shown in FIG. 20 and FIG. A dimension object edit processing routine is executed. In addition, about the process similar to the 1st-5th embodiment, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

  In step S600, speech recognition processing is performed on the input speech to convert the speech into text. The process waits in step S600 until a voice is input.

  In step S204, the three-dimensional object is formed so that a three-dimensional object representing the post-deformation 3D object model or the 3D object model obtained in step S103 or the edited 3D object model obtained in step S526 is formed. The modeling unit 230 is controlled and the three-dimensional object editing process routine is terminated.

  As described above, according to the three-dimensional object generation device according to the sixth embodiment, in order to analyze a text input by speech recognition and form a three-dimensional structure in real space as a real object, A user can obtain a three-dimensional structure from an extremely easy interface. In addition, a three-dimensional object can be edited using text input by voice recognition.

  Note that a three-dimensional structure that represents the 3D object model after deformation may be formed, and voice input related to an editing operation may be received with respect to the formed three-dimensional structure.

[Seventh Embodiment]
<System configuration>
Next, a three-dimensional object generation device according to a seventh embodiment will be described. In addition, about the part which becomes the structure and effect | action similar to 1st-6th embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The seventh embodiment is different from the sixth embodiment in that a voice input representing an initial text is accepted in a situation where a 3D object model already exists.

  As illustrated in FIG. 22, the three-dimensional object generation device 700 according to the seventh embodiment includes an input unit 10, a calculation unit 720, a model output unit 30, and a three-dimensional modeling unit 230.

  The input unit 10 accepts input of data of a 3D object model and accepts voice input representing initial text related to attribute values of the 3D object model. For example, an input of data of a 3D object model of a car is accepted and a voice input representing an initial text “yellow car” is accepted. In addition, the input unit 10 receives a voice input representing a dialog text related to an editing operation. For example, a voice input representing a dialog text “more thinly” is accepted. In addition, the input unit 10 receives a voice input representing an end text related to the end process. For example, a voice input representing an end text “end” is received. Note that the audio input that is input for the first time (before the output of the 3D object model) is an audio input that represents the initial text regarding the attribute value that is input together with the data of the 3D object model, and is input after the 3D object model is output The voice input is a voice input representing a dialogue text related to an editing operation or a voice input representing a termination text related to a termination process. The model output unit 30 outputs the input 3D object model.

  The computing unit 720 includes a speech recognition unit 221, an initial text analysis unit 721, a model transformation unit 724, a dialog text analysis unit 626, a model editing unit 528, and a 3D modeling control unit 225.

  The initial text analysis unit 721 identifies the attribute value related to the target 3D object model from the initial text converted by the speech recognition unit 221 for the first input speech.

  The model deformation unit 724 deforms the input 3D object model according to the attribute value identified by the initial text analysis unit 721, and outputs the deformed 3D object model by the model output unit 30.

  As shown in FIG. 23, the dialog text analysis unit 726 includes an end determination unit 550, a word extraction unit 552, an operation candidate extraction unit 554, a word selection unit 556, an operation ranking unit 557, a connection unit 558, and a change amount calculation unit 560. , A word pair operation conversion database 570, a natural sentence set database 572, a keyword pair operation candidate correspondence database 574, and a conjunction particle database 576.

<Operation of three-dimensional object generation device>
Next, the operation of the three-dimensional object generation device 700 according to the seventh embodiment of the present invention will be described. First, when 3D object model data is input to the three-dimensional object generation device 700, the model output unit 30 outputs the input 3D object model. Then, when sound representing initial text including attribute values related to the 3D object model is input to the 3D object generation device 700, the 3D object generation device 700 causes the 3D object editing processing routine shown in FIGS. Is executed. In addition, about the process similar to 1st-6th embodiment, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

  In step S201, a speech recognition process is performed on the input speech to convert the speech to an initial text. In step S701, the initial text obtained in step S201 is analyzed to identify attribute values related to the 3D object model.

  In step S302, the input 3D object model data is transformed according to the attribute value identified in step S301 and is output by the model output unit 30.

  As described above, according to the three-dimensional object generation device according to the seventh embodiment, the text input by the speech recognition is analyzed, the three-dimensional object is deformed, and the three-dimensional object is converted into the real space as a real object. In order to form a three-dimensional structure, the user can obtain a three-dimensional structure from an extremely easy interface. Further, the three-dimensional object can be edited by the input voice.

  In the above embodiment, the case where voice input is accepted has been described as an example. However, the present invention is not limited to this, and text input may be accepted.

  Further, the edited 3D object model may be simply output by the model output unit without forming the three-dimensional structure representing the edited 3D object model.

  Note that, as in a ninth embodiment to be described later, a voice input may be received to determine whether the text converted by the voice recognition unit is an initial text or a dialogue text.

[Eighth Embodiment]
<System configuration>
Next, a three-dimensional object generation device according to an eighth embodiment will be described. In addition, about the part which becomes the structure and effect | action similar to 1st-7th Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The eighth embodiment differs from the sixth embodiment in that it is classified into the object name of the 3D object model as it is with voice input, and the editing operation is identified as it is with voice input. ing.

  As illustrated in FIG. 25, the three-dimensional object generation device 800 according to the eighth embodiment includes an input unit 10, a calculation unit 820, a model output unit 30, and a three-dimensional modeling unit 230.

  The input unit 10 receives a voice input related to the 3D object model. Further, the input unit 10 receives a voice input related to an editing operation. Accepts voice input related to termination processing. Note that the first voice input (before the output of the 3D object model) is an initial voice input related to the 3D object model, and the voice input input after outputting the 3D object model is an interactive voice input related to the editing operation. Or it is set as the end voice input regarding the end process.

  The calculation unit 820 includes a voice analysis unit 421, a 3D object model database 22, a model acquisition unit 423, a model deformation unit 524, a three-dimensional modeling control unit 225, a dialog voice analysis unit 821, and a model editing unit 828.

  The voice analysis unit 421 identifies the object name and attribute value related to the target 3D object model from the voice signal input as the initial voice input, and outputs the analysis result to the model acquisition unit 423.

  Specifically, the audio signal input as the initial audio input is classified into object names stored in the 3D object model database 22. As for the classification model, a plurality of learning data in which audio signals and class labels (object names) are associated one-to-one are prepared, and the classification model is learned based on the learning data.

  Also, the audio signal input as the initial audio input is classified into attribute values related to the 3D object model. As for the classification model, a plurality of learning data in which a speech signal and a class label (attribute value) are associated one-to-one are prepared, and the classification model is learned based on the learning data.

  In addition, the dialog voice analysis unit 821 identifies whether the voice signal input after outputting the 3D object model is an end voice signal related to the end process, and outputs the analysis result to the model editing unit 828. In addition, the dialog voice analysis unit 821 identifies and analyzes the editing operation from the voice signal input as the dialog voice input after outputting the 3D object model and the object name and attribute value identified by the voice analysis unit 421. The result is output to the model editing unit 828.

  Specifically, the input audio signal is classified into whether or not it is an end audio input related to an end process. For a classification model, a plurality of learning data in which a speech signal and a class label (whether or not the end speech input is input) are prepared in a one-to-one correspondence are prepared, and the classification model is learned based on the learning data. Good.

  Further, the combination of the audio signal input as the dialogue audio input, the object name, and the attribute value is classified as an editing operation. As for the classification model, a plurality of learning data in which a combination of a speech signal, an object name, and an attribute value is associated with a class label (editing operation) in one-to-one correspondence is prepared, and the classification model is learned based on the learning data You just have to.

  When the identification result about the editing operation is input from the dialog voice analysis unit 821, the model editing unit 828 reads the 3D object model stored in the memory and follows the editing operation identified by the dialog voice analysis unit 821. The read 3D object model is edited, stored in a memory (not shown), and output by the model output unit 30. Further, the model editing unit 828 intentionally divides the edited 3D object model in order to speed up the subsequent three-dimensional modeling. For example, the axial direction in which the size of the edited 3D object model is the shortest is obtained from an arbitrary axial direction in the three-dimensional space, and the edited 3D object model is divided into N in the obtained axial direction. At this time, the division is performed so that the number of objects does not become larger than double. In addition, marking is performed on the divided objects so that the adhesion surface is uniquely determined. Further, when a result classified as the end voice input is input from the voice analysis unit 821, the 3D object model finally stored in the memory is output to the three-dimensional modeling control unit 225.

<Operation of three-dimensional object generation device>
Next, the operation of the three-dimensional object generation device 800 according to the eighth embodiment of the present invention will be described. First, when a voice representing a text representation and an attribute value related to a 3D object model to be generated is input as an initial voice input to the three-dimensional object generation device 800, the three-dimensional object generation device 800 causes the three-dimensional object shown in FIG. An editing process routine is executed. In addition, about the process similar to 1st-7th embodiment, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

  In step S <b> 801, an audio signal input as an initial audio input is received, and the received audio signal is classified into object names stored in the 3D object model database 22. Also, the audio signal input as the initial audio input is classified into attribute values related to the 3D object model.

  In step S803, the 3D object model that matches the object name classified in step S801 is read from the 3D object model database 22, transformed to correspond to the attribute value classified in step S803, and output by the model output unit 30.

  In step S805, the audio signal input to the post-transform 3D object model output in step S803 or the post-edit 3D object model output in step S811 described below is received. It classifies whether it is.

  In step S807, it is determined whether or not it is classified as the end voice input in step S805. When the classification result is an end voice input, the modified 3D object model deformed in step S803 or the edited 3D object model edited in step S809 is output to the three-dimensional modeling control unit 225. If the classification result is not the end voice input, it is determined that the voice signal input as the dialog voice input has been received, and the process proceeds to step S808.

  In step S808, the audio signal is classified into one of a plurality of editing operations based on the audio signal received in step S805 and the object name and attribute value classified in step S801.

  In step S809, based on the editing operation classified in step S805, the modified 3D object model deformed in step S803 or the edited 3D object model edited in the previous step S809 is edited.

  In step S811, the edited 3D object model edited in step S809 is output by the model output unit 30, and the process returns to step S805.

  As described above, according to the three-dimensional object generation device according to the eighth embodiment, it is very easy for the user to analyze a voice input and form a three-dimensional structure in real space as a real object. 3D objects can be obtained from a simple interface. Further, the three-dimensional object can be edited by the input voice.

  Note that, as in a ninth embodiment to be described later, a voice input may be received to determine whether the voice input is an initial voice input or an interactive voice input.

[Ninth Embodiment]
<System configuration>
Next, a three-dimensional object generation device according to a ninth embodiment will be described. In addition, about the part which becomes the structure and effect | action similar to 1st-8th embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted.

  The ninth embodiment is different from the sixth embodiment in that it is determined whether the input speech recognition result is an initial text or a dialog text.

  As illustrated in FIG. 27, the three-dimensional object generation device 900 according to the ninth embodiment includes an input unit 10, a calculation unit 920, a model output unit 30, and a three-dimensional modeling unit 230.

  The input unit 10 receives a voice input representing initial text related to the 3D object model. In addition, the input unit 10 receives a voice input representing a dialog text related to an editing operation. In addition, the input unit 10 receives a voice input representing an end text related to the end process.

  The calculation unit 220 includes a voice recognition unit 221, an initial dialogue determination unit 921, an initial text analysis unit 521, a 3D object model database 22, a model acquisition unit 23, a model transformation unit 524, a three-dimensional modeling control unit 225, and a dialogue text analysis unit 926. , And a model editing unit 528.

  The voice recognition unit 221 performs voice recognition processing on the input voice and converts it into text (for example, “10 cm yellow car”). Note that the content of the speech recognition process may be conventional speech recognition (for example, speech recognition using a hidden Markov model or HMM), and any method may be used.

  The initial dialogue determination unit 921 performs morphological analysis on the text of the speech recognition processing result input from the speech recognition unit 221 as shown in FIG. Then, when a keyword associated with the termination process is input based on the form analysis result, the text is output to the dialog text analysis unit 926. Further, when the keyword associated with the termination process is not input, the input text is classified into the initial text or the dialog text by rule base or machine learning based on the morphological analysis result. When it is determined that the initial text is input, the text is output to the initial text analysis unit 521. When it is determined that the dialog text is input, the text is output to the dialog text analysis unit 926. To do. When classifying by rule base, if the last model name included in the text of the speech recognition processing result stored in the memory is different from the last noun of the input text, the initial text is used. It is determined that there is, and otherwise, it is determined that the text is a dialog text.

  Also, when classifying using machine learning, the previous text analysis result or morpheme analysis result stored in the memory is featured, and based on an identification model learned in advance, Categorize whether initial text or interactive text. Here, in the case of performing the feature by bigram, when the text “red car” is input, the feature becomes “car-EOS, noun-EOS”. However, EOS represents a sentence end symbol. Also, in the case of trigram feature, if the text “more thin” is input, the feature becomes “thin-ku-EOS” “adjective stem-adjective suffix-continuous”.

  As shown in FIG. 29, the dialog text analysis unit 926 includes an end determination unit 550, a word extraction unit 552, an operation candidate extraction unit 554, a word selection unit 556, an operation ranking unit 557, a connection unit 558, and a change amount calculation unit 560. , A word pair operation conversion database 570, a natural sentence set database 572, a keyword pair operation candidate correspondence database 574, and a conjunction particle database 576.

  The end determination unit 550 determines that the input text is the end text when the initial dialogue determination unit 921 determines that the keyword associated with the end process has been input.

<Operation of three-dimensional object generation device>
Next, the operation of the three-dimensional object generation device 900 according to the ninth embodiment will be described. First, when sound is input to the three-dimensional object generation device 900, the three-dimensional object generation processing routine shown in FIG. In addition, about the process similar to 1st-8th embodiment, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

  In step S901, it is determined whether the text resulting from the speech recognition process in step S201 includes a keyword associated with the end process, and whether the text is an initial text or a dialog text.

  If it is determined in step S902 that the result determined in step S901 is the initial text, the process proceeds to step S501. If it is determined that the result is not the initial text, the process proceeds to step S601.

  As described above, according to the three-dimensional object generation device according to the ninth embodiment, in order to analyze a text input by speech recognition and form a three-dimensional structure in real space as a real object, A user can obtain a three-dimensional structure from an extremely easy interface. In addition, a three-dimensional object can be transformed by text input by voice recognition. Further, by classifying the initial text or the dialogue text according to the input voice, a three-dimensional object is subsequently generated based on the initial text, or the three-dimensional object is edited based on the dialogue text. be able to.

  The present invention is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the gist of the present invention.

  For example, as shown in FIG. 31, assuming a character in the 3D object model, the 3D object model that displays the spoken character is output as it is, or a 3D object model of the 3D object model that displays the spoken character is formed. You may do it.

  In the above embodiment, the case where text and speech are classified into object names using a classification model learned by machine learning is described as an example. However, the present invention is not limited to this. Audio may be classified into object names using a rule base.

  In the present specification, the embodiment has been described in which the program is installed in advance. However, the program can be provided by being stored in a computer-readable recording medium.

10 input unit 20, 220, 320, 420, 520, 620, 720, 820, 920 arithmetic unit 21, 321 text analysis unit 22 3D object model database 23, 423 model acquisition unit 24, 324, 524, 724 model transformation unit 30 Model output unit 100, 200, 300, 400, 500, 600, 700, 800, 900 3D object generation device 221 Speech recognition unit 225 3D modeling control unit 230 3D modeling unit 421 Speech analysis unit 521, 721 Initial text analysis Units 526, 626, 726, 926 Dialogue text analysis unit 528, 828 Model editing unit 550 Termination determination unit 552 Word extraction unit 554 Operation candidate extraction unit 556 Word selection unit 557 Operation ranking unit 558 Connection unit 560 Change amount calculation unit 570 Word Database for operation conversion Scan 572 natural sentence aggregated database 574 keyword pair candidate operation correspondence database 576 predicate Database 821 interacts voice analysis unit 921 initial dialogue determination unit

Claims (9)

Dialog input that is text and speech including at least one word, input to the output of the object model generated corresponding to the initial input that is at least one of text and speech including the input word, and the initial A dialog text analysis unit for determining an editing operation represented by the dialog input based on the input;
A model editing unit for editing the generated object model based on the editing operation determined in the dialog text analysis unit;
An object generation device including: Dialog input that is text and speech that includes at least one word input to the output of the object model generated in response to the initial input that is at least one of text and speech that includes two or more input words And, based on the initial input, using the ease of semantic connection between each of the at least one word of the dialog input and each of the two or more words of the initial input, A word selection unit for selecting a word representing an edit target from the two or more words of the input;
A dialog text analysis unit that determines an editing operation represented by the dialog input based on the at least one word of the dialog input and the word selected by the word selection unit;
A model editing unit for editing the generated object model based on the editing operation determined in the dialog text analysis unit;
An object generation device including: An operation candidate extraction unit that extracts at least one editing operation candidate from a plurality of predetermined editing operations based on the dialogue input;
The word selection unit is configured to share words between each of the words representing the at least one editing operation candidate extracted by the operation candidate extraction unit and each of the two or more words of the initial input. The object generation device according to claim 2, wherein a word representing the editing target is selected from the two or more words of the initial input using a starting relation.   The object generation device according to claim 1, wherein the object model generated corresponding to the initial input is a three-dimensional object model. An initial dialog determination unit that determines whether at least one of text and speech including the input word is the initial input or the dialog input;
The object generation device according to claim 1, wherein the dialog text analysis unit determines an editing operation represented by the dialog input when the initial dialog determination unit determines that the dialog input is the dialog input. .   The object generation apparatus according to claim 1, further comprising a three-dimensional modeling unit that forms a three-dimensional modeled object based on the object model edited by the model editing unit. An object generation method in an object generation device including a dialog text analysis unit and a model editing unit,
The dialog text analysis unit is configured to input text and speech including at least one word that is input in response to an output of the object model generated corresponding to an initial input that is at least one of text and speech including the input word. Based on a certain dialog input and the initial input, an editing operation represented by the dialog input is determined,
The model editing unit is an object generation method for editing the generated object model based on an editing operation determined by the dialog text analysis unit. An object generation method in an object generation device including a word selection unit, a dialog text analysis unit, and a model editing unit,
The word selection unit is a text including at least one word input with respect to an output of an object model generated corresponding to an initial input that is at least one of a text including two or more input words and a voice. And, based on the initial input and the dialogue input being speech, the ease of semantic connection between each of the at least one word of the dialogue input and each of the two or more words of the initial input To select a word representing an edit target from the two or more words of the initial input,
The dialog text analysis unit determines an editing operation represented by the dialog input based on the at least one word of the dialog input and the word selected by the word selection unit;
The model editing unit is an object generation method for editing the generated object model based on an editing operation determined by the dialog text analysis unit. The program for functioning a computer as each part of the object production | generation apparatus in any one of Claims 1-5.