US 11,941,739 B1
Object deformation network system and method
Sarah Radzihovsky, Boulder, CO (US); Fernando Ferrari de Goes, Kensington, CA (US); and Mark Meyer, San Francisco, CA (US)
Assigned to PIXAR, Emeryville, CA (US)
Filed by Pixar, Emeryville, CA (US)
Filed on Jan. 5, 2022, as Appl. No. 17/569,396.
Claims priority of provisional application 63/134,080, filed on Jan. 5, 2021.
Int. Cl. G06T 13/40 (2011.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06T 7/70 (2017.01); G06T 17/20 (2006.01)
CPC G06T 13/40 (2013.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/70 (2017.01); G06T 17/205 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving a set of input values for posing an initial mesh defining a surface of a three-dimensional object;
providing the set of input values to a neural network trained based upon training posed meshes generated using a rigging model to generate mesh offset values defined in three-dimensional space based upon the set of input values and the initial mesh, the neural network comprising an input layer, an output layer, and a plurality of intermediate layers;
generating, by the output layer of the neural network, a set of offset values corresponding to a set of three-dimensional target points based on the set of input values;
applying the offset values to the initial mesh to generate a posed mesh; and
outputting the posed mesh for generating an animation frame, wherein the neural network is trained by:
executing the rigging model based upon a set of training values to pose a training input mesh to generate a training deformed mesh;
providing the set of training values as input to the input layer of the neural network;
generating, based on output of the neural network, a training estimated mesh; and
updating parameters of the neural network to minimize a loss function between the training estimated mesh and the training deformed mesh.