Motion capture data prediction system for video game development

1 . A method for predicting motion capture data implemented by a video game development system comprising one or more processors, the method comprising:

obtaining, by one or more of the processors of the video game development system, motion capture data as generated by a motion capture system; wherein the motion capture data comprises one or more missing values; and

generating, by one or more of the processors of the video game development system, a predicted value for the one or more missing values, wherein generating a predicted value comprises:

processing the motion capture data using one or more machine learning models to determine the predicted value; wherein the one or more machine learning models comprise one or more neural networks including one or more one-dimensional convolutional layers configured to perform one dimensional convolution along a temporal axis with the motion capture data arranged according to a plurality of dimensions comprising: (i) a temporal dimension and (ii) a spatial dimension formed by the positional elements for each marker of the motion capture data.

2 . The method of claim 1 , wherein generating a predicted value further comprises hip-centering the motion capture data prior to processing by the one or more machine learning models.

3 . The method of claim 2 , wherein the motion capture data comprises data associated with one or more hip markers;

wherein hip-centering the motion capture data comprises:

determining a position of the hips based upon the data associated with the one or more hip markers; and

centering the motion capture data based upon the determined position of the hips.

4 . The method of claim 2 , wherein generating a predicted value further comprises de-centering the predicted value determined by the one or more machine learning models.

5 . The method of claim 4 , wherein the de-centering is based upon the determined position of the hips.

6 . The method of claim 1 , wherein processing the motion capture data by the one or more machine learning models to determine a predicted value comprises iteratively refining the predicted value by the one or more machine learning models.

7 . The method of claim 1 , wherein generating a predicted value further comprises removing values from the motion capture data based upon a temporal window determined from the one or more missing values prior to processing the motion capture data by the one or more machine learning models.

8 . The method of claim 1 , wherein the one or more machine learning models are configured to provide predicted values for all existing values of the motion capture data.

9 . A video game development system comprising:

one or more processors; and

one or more computer readable storage media comprising processor readable instructions to cause the one or more processors to carry out a method comprising:

obtaining motion capture data as generated by a motion capture system;

wherein the motion capture data comprises one or more missing values; and

generating a predicted value for the one or more missing values, wherein generating a predicted value comprises:

processing the motion capture data using one or more machine learning models to determine the predicted value; wherein the one or more machine learning models comprise one or more neural networks including one or more one-dimensional convolutional layers configured to perform one dimensional convolution along a temporal axis with the motion capture data arranged according to a plurality of dimensions comprising: (i) a temporal dimension and (ii) a spatial dimension formed by the positional elements for each marker of the motion capture data.

10 . The system of claim 9 , wherein generating a predicted value further comprises hip-centering the motion capture data prior to processing by the one or more machine learning models.

11 . The system of claim 10 , wherein the motion capture data comprises data associated with one or more hip markers;

wherein hip-centering the motion capture data comprises:

determining a position of the hips based upon the data associated with the one or more hip markers; and

centering the motion capture data based upon the determined position of the hips.

12 . The system of claim 10 , wherein generating a predicted value further comprises de-centering the predicted value determined by the one or more machine learning models.

13 . The system of claim 9 , wherein processing the motion capture data by the one or more machine learning models to determine a predicted value comprises iteratively refining the predicted value by the one or more machine learning models.

14 . The system of claim 9 , wherein generating a predicted value further comprises removing values from the motion capture data based upon a temporal window determined from the one or more missing values prior to processing the motion capture data by the one or more machine learning models.

15 . The system of claim 9 , wherein the one or more machine learning models are configured to provide predicted values for all existing values of the motion capture data.

16 . One or more non-transitory computer-readable storage media comprising instructions which, when executed by one or more processors of a video game development system, cause the one or more processors to carry out a method comprising:

obtaining motion capture data as generated by a motion capture system; wherein

the motion capture data comprises one or more missing values; and

generating a predicted value for the one or more missing values, wherein generating a predicted value comprises:

processing the motion capture data using one or more machine learning models to determine the predicted value; wherein the one or more machine learning models comprise one or more neural networks including one or more one-dimensional convolutional layers configured to perform one dimensional convolution along a temporal axis with the motion capture data arranged according to a plurality of dimensions comprising: (i) a temporal dimension and (ii) a spatial dimension formed by the positional elements for each marker of the motion capture data.