Patent ID: 11887233
Assignee: DIGITAL DOMAIN VIRTUAL HUMAN (US), INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 17:
18. A method for preparing training data for training a neural network to simulate deformations of a surface of a CG character, the method comprising:
obtaining a distribution of joint angles and/or bone positions of a CG character over a set of animation data comprising a plurality of frames;
randomly generating a plurality of random poses according to the distribution of joint angles and/or bone positions;
generating a high-fidelity deformation of the surface of the CG character for each of the plurality of random poses;
transforming each of the high-fidelity deformations from a respective pose coordinate system to a rest pose coordinate system to obtain a plurality of warped rest poses, each warped rest pose corresponding to one of the high-fidelity deformations and one of the random poses and each warped rest pose parameterized at least in part by a three-dimensional (3D) surface mesh comprising a plurality of vertices;
determining an approximation weight for each vertex of each of the plurality of warped rest poses;
decomposing the plurality of warped rest poses to obtain: a decomposition neutral vector, a set of decomposition basis (blendshape) vectors and, for each warped rest pose, a set of decomposition weights;
wherein, for each warped rest pose, the corresponding set of decomposition weights together with the decomposition neutral vector and the set of decomposition basis (blendshape) vectors can be used to at least approximately reconstruct the warped rest pose;
wherein decomposing the plurality of warped rest poses is based at least in part on the approximation weights; and
determining the training data to comprise the plurality of random poses and, for each random pose, the corresponding set of decomposition weights.