Data-driven physics-based models with implicit actuations

Number: 12223577
Published: 2025-02-11
Filed: 2023-01-25
Assignee: Disney Enterprises, INC.
Inventors: Zoss; Gaspard et al.
CPC: G06T13/40; G06T17/20; G06T19/20
Verdict: Low Notable software
Source: Google Patents · FreePatentsOnline

The keeper's note

Physics-based implicit-actuation character modeling technique.

Abstract

One embodiment of the present invention sets forth a technique for generating actuation values based on a target shape such that the actuation values cause a simulator to output a simulated soft body that matches the target shape. The technique includes inputting a latent code that represents a target shape and a point on a geometric mesh into a first machine learning model. The technique further includes generating, via execution of the first machine learning model, one or more simulator control values that specify a deformation of the geometric mesh, where each of the simulator control values is based on the latent code and corresponds to the input point, and generating, via execution of the simulator, a simulated soft body based on the one or more simulator control values and the geometric mesh. The technique further includes causing the simulated soft body to be outputted to a computing device.

Background

BACKGROUND Field of the Various Embodiments (1) Embodiments of the present disclosure relate generally to machine learning and computer animation and, more specifically, to data-driven physics-based models with implicit actuations. DESCRIPTION OF THE RELATED ART (2) In computer animation, a model of an object being animated is constructed in memory and used to generate a sequence of images that depict movement of the object over a period of time. Soft body animation refers to computer animation of objects that have deformable surfaces, such as human faces. An active soft body can deform in response to contact with other objects, or as a result of contraction of muscles under the surface of the active soft body. (3) A soft body model can be constructed to simplify the process of animating an active soft body. Once the model has been constructed, the active soft body's appearance can be controlled by specifying parameters for the model. For example, a soft body model can simulate the deformations of an active soft body caused by internal actuations, such as muscle contraction, or external stimuli such as contacts and external forces. (4) Manually creating such a soft body model is a very tedious and error prone process, since it typically requires an extensive knowledge of the underlying anatomy as well as expertise in translating anatomical features to a simulation model. Soft body models can also be created using automated processes. In some physics-based approaches, the proc

Claims

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. || 17. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: 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. || 20. A system, comprising: one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of: 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.