US 12,223,577 B2
Data-driven physics-based models with implicit actuations
Gaspard Zoss, La Tour-de-Peilz (CH); Baran Gözcü, Burbank, CA (US); Barbara Solenthaler, Burbank, CA (US); Lingchen Yang, Burbank, CA (US); and Byungsoo Kim, Burbank, CA (US)
Assigned to Disney Enterprises, INC., Burbank, CA (US); and ETH Zürich (Eidgenössische Technische Hochschule Zürich), Zürich (CH)
Filed by DISNEY ENTERPRISES, INC., Burbank, CA (US); and ETH Zürich (Eidgenössische Technische Hochschule Zürich), Zürich (CH)
Filed on Jan. 25, 2023, as Appl. No. 18/159,651.
Claims priority of provisional application 63/303,452, filed on Jan. 26, 2022.
Prior Publication US 2023/0237725 A1, Jul. 27, 2023
Int. Cl. G06T 13/00 (2011.01); G06T 13/40 (2011.01); G06T 17/20 (2006.01); G06T 19/20 (2011.01)
CPC G06T 13/40 (2013.01) [G06T 17/20 (2013.01); G06T 19/20 (2013.01); G06T 2210/36 (2013.01); G06T 2219/2021 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving a latent code that represents a target shape;
inputting the latent code and an input point on a geometric mesh associated with the target shape into a first machine learning model;
generating, via execution of the first machine learning model, one or more simulator control values that specify a deformation of the geometric mesh, wherein each of the simulator control values is based on the latent code and corresponds to the input point on the geometric mesh;
generating, via execution of a differentiable simulator, a simulated soft body based on the one or more simulator control values and the geometric mesh; and
causing the simulated soft body to be outputted to a computing device, wherein the simulated soft body is used to perform one or more simulation operations.