Vectorized caricature avatar generator

1. In a digital medium environment, a method implemented by at least one computing device, the method comprising:

receiving an image including a user face;

identifying a segment of the image that identifies a facial feature of the user face;

generating, for each of multiple face parameters, a face parameter value by processing a first portion of the user face that excludes the facial feature using a first machine learning system;

determining segment parameter values for the segment of the image by processing a second portion of the user face that includes the facial feature and excludes the first portion of the user face using a second machine learning system that is separately trained from the first machine learning system to determine the segment parameter values for the facial feature, the second machine learning system trained using training data that includes corresponding segments of multiple user images that identify the facial feature in the multiple user images and known segment parameter values for the corresponding segments of caricature avatars corresponding to the multiple user images, the second machine learning system having been trained using the known segment parameter values that are changed from default values during manual generation of the caricature avatars while masking the known segment parameter values that are unchanged from the default values during the manual generation of the caricature avatars; and

generating, from the face parameter values and the segment parameter values, a vector image that is a caricature avatar of a user depicted in the image.

2. The method of claim 1 , wherein the generating the face parameter value includes generating the face parameter value using the first machine learning system trained using training data that includes a facial texture of each of the multiple user images and known face parameter values corresponding to each of the multiple user images.

3. The method of claim 2 , the known face parameter values and the known segment parameter values having been generated manually by generating the caricature avatars for users depicted in the multiple user images.

4. The method of claim 2 , further comprising training the first machine learning system by:

receiving a user image of the multiple user images;

generating the facial texture of the first portion of an additional user face depicted in the user image that excludes the facial feature;

determining, using the first machine learning system, additional face parameter values of the user image based on the facial texture; and

updating the first machine learning system based on a loss between the additional face parameter values and the known face parameter values of the user image.

5. The method of claim 1 , the determining the segment parameter values including:

identifying, using the second machine learning system, intermediary segment parameter values for the segment of the image;

determining one of the caricature avatars having the known segment parameter values of a corresponding segment with a smallest difference to the intermediary segment parameter values of the segment of the image; and

determining, as the segment parameter values, the known segment parameter values of the determined one of the caricature avatars.

6. The method of claim 1 , wherein the training data includes a facial texture of the corresponding segments that identify the facial feature in the multiple user images and the known segment parameter values.

7. The method of claim 1 , further comprising:

receiving user input changing at least one of the face parameter values; and

generating, from the face parameter values including the at least one changed face parameter value, a new caricature avatar of the user.

8. The method of claim 1 , further comprising training the second machine learning system by:

receiving a user image of the multiple user images;

identifying a corresponding segment of the user image that identifies the facial feature;

generating one or more rotated versions of the corresponding segment of the user image;

generating, using the second machine learning system, additional segment parameter values from the one or more rotated versions of the corresponding segment; and

updating the second machine learning system based on a loss between the additional segment parameter values generated from the one or more rotated versions of the corresponding segment and the known segment parameter values for the corresponding segment of the user image.

9. The method of claim 1 , wherein the generating the face parameter value includes:

identifying dense key points of the image representing a geometry of the user face;

extracting a facial texture of the user face that is a set of vectors representing the user face; and

generating, for each of the multiple face parameters, the face parameter value based on the dense key points and the facial texture.

10. The method of claim 1 , further comprising:

receiving user input changing at least one of the segment parameter values; and

generating, from the segment parameters including the at least one changed segment parameter value, a new caricature avatar of the user.

11. The method of claim 1 , further comprising training the second machine learning system by:

receiving a user image of the multiple user images;

generating a facial texture of a corresponding segment of the user image that identifies the facial feature;

determining, using the second machine learning system, additional segment parameter values of the corresponding segment based on the facial texture; and

updating the second machine learning system based on a loss between the additional segment parameter values and the known segment parameter values for the corresponding segment of the user image.

12. The method of claim 1 , wherein the facial feature is hair, facial hair, or eyeglasses.

13. In a digital medium environment, a computing device comprising:

a processor; and

computer-readable storage media having stored thereon multiple instructions of an application that, responsive to execution by the processor, cause the processor to:

receive an image including a user face;

identify a segment of the image that identifies a facial feature of the user face;

generate, for each of multiple face parameters, a face parameter value by processing a first portion of the user face that excludes the facial feature using a first machine learning system;

identify, for the segment, segment parameter values by processing a second portion of the user face that includes the facial feature and excludes the first portion of the user face using a second machine learning system that is separately trained from the first machine learning system to identify the segment parameter values for the facial feature, the second machine learning system trained using training data that includes corresponding segments of multiple user images that identify the facial feature in the multiple user images and known segment parameter values for the corresponding segments of caricature avatars corresponding to the multiple user images, the second machine learning system having been trained using the known segment parameter values that are changed from default values during manual generation of the caricature avatars while masking the known segment parameter values that are unchanged from the default values during the manual generation of the caricature avatars; and

generate, from the face parameter values and the segment parameter values, a vector image that is a caricature avatar of a user depicted in the image.

14. The computing device of claim 13 , wherein to generate the face parameter value, the multiple instructions cause the processor to generate the face parameter value using the first machine learning system trained using training data that includes a facial texture of each of the multiple user images and known face parameter values corresponding to each of the multiple user images.

15. The computing device of claim 14 , the known face parameter values and the known segment parameter values having been generated manually by generating caricature avatars for users in the multiple user images.

16. The computing device of claim 13 , wherein to identify the segment parameter values, the multiple instructions cause the processor to:

identify, using the second machine learning system, intermediary segment parameter values for the segment of the image;

determine one of the caricature avatars having the known segment parameter values with a smallest difference to the intermediary segment parameter values of the segment of the image; and

determine, as the segment parameter values, the known segment parameter values of the determined one of the caricature avatars.

17. The computing device of claim 16 , the training data includes a facial texture of the corresponding segments that identifying the facial feature in the multiple user images and the known segment parameter values.

18. The computing device of claim 13 , wherein the multiple instructions further cause the processor to:

receive user input changing at least one of the face parameter values; and

generate, from the face parameter values including the at least one changed face parameter value, a new caricature avatar of the user.

19. A non-transitory computer-readable medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising:

generating a segment of an image that depicts a user face, wherein the segment identifies a facial feature of the user face;

identifying, for each of multiple face parameters, a face parameter value by processing a first portion of the user face that excludes the facial feature using a first machine learning system;

determining segment parameter values for the segment by processing a second portion of the user face that includes the facial feature and excludes the first portion of the user face using a second machine learning system that is separately trained from the first machine learning system to determine the segment parameter values for the facial feature, the second machine learning system trained using training data that includes corresponding segments of multiple user images that identify the facial feature in the multiple user images and known segment parameter values for the corresponding segments of caricature avatars corresponding to the multiple user images, the second machine learning system having been trained using the known segment parameter values that are changed from default values during manual generation of the caricature avatars while masking the known segment parameter values that are unchanged from the default values during the manual generation of the caricature avatars; and

generating, from the face parameter values and the segment parameter values, a vector image that is a caricature avatar of a user depicted in the image.

20. The non-transitory computer-readable medium of claim 19 , the operations further comprising:

receiving user input changing at least one of the face parameter values; and

generating, from the face parameter values including the at least one changed face parameter value, a new caricature avatar of the user.