DATA-DRIVEN MODELING OF SECONDARY MOTION DYNAMICS USING GAUSSIAN SPLATTING

Number: 20260195954
Published: 2026-07-09
Filed: 2025-01-06
Assignee: DISNEY ENTERPRISES, INC.
Inventors: BRADLEY; Derek Edward, WEISS; Sebastian Klaus, CHANDRAN; Prashanth, ZOSS; Gaspard, BAEZA ROJO; Irene, XIA; Yitong
CPC: G06T13/40; G06T15/20; G06T17/00; G06T19/20; G06T2219/2004; G06T2219/2012; G06T2219/2016
Verdict: Low Notable software
First reported: 2026-W29 (2026-07-15)
Source: Google Patents · FreePatentsOnline

The keeper's note

The present invention sets forth techniques for predicting motion in a 3D model.

Abstract

The present invention sets forth techniques for predicting motion in a 3D model. The disclosed techniques include receiving one or more Gaussian primitives representing a 3D scene and receiving one or more control inputs describing one or more primary motions associated with one or more objects. The techniques also include generating, via a first trained machine learning model, a dynamic state based on the one or more control inputs, and generating, via a second trained machine learning model, one or more deformed Gaussian primitives based on the dynamic state and the one or more Gaussian primitives. The techniques further include generating a 2D representation of the 3D scene, and generating an output sequence based on the 2D representation, wherein the output sequence depicts both the primary motions associated with the one or more objects and one or more secondary motions associated with the one or more objects.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to computer animation and, more specifically, to techniques for modeling secondary motion dynamics in an animated representation of a 3D character model. Description of the Related Art

Animating a digital avatar or other 3D character model is a common task in computer animation. Animating a 3D character model requires a visually realistic and computationally efficient representation of the character model, as well as accurate predictions of deformation and motion.

Deformation may include quasi-static deformation, based on one-to-one mappings of artistic control inputs to deformations of the 3D character model at a single point in time. Artistic control inputs may include, e.g., explicit motion descriptions of a skeletal structure associated with the 3D character model, simulated muscle actuations in the 3D character model, or the application of one or more blend shapes to the 3D character model. Quasi-static motion, including the motion of limbs, joints, or other simulated features included in the 3D character model, may be referred to as “primary motion.”

Many real-world deformations are dynamic, rather than quasi-static. For example, long hair, loose skin, or baggy clothing may continue to move even after an underlying body motion, such as the movement of a head, limb, or other skeletal feature included in a 3D character model, has ceased. These dyn

Claims

1. A computer-implemented method for predicting motion in a 3D model, the computer-implemented method comprising: receiving one or more Gaussian primitives representing a 3D scene including one or more objects; receiving one or more control inputs describing one or more primary motions associated with the one or more objects; generating, via a first trained machine learning model, a dynamic state based on the one or more control inputs; generating, via a second trained machine learning model, one or more deformed Gaussian primitives based on the dynamic state and the one or more Gaussian primitives; generating, via a renderer, a 2D representation of the 3D scene based on the one or more deformed Gaussian primitives and a virtual camera viewpoint; and generating an output sequence based at least on the 2D representation, wherein the output sequence depicts both the one or more primary motions associated with the one or more objects and one or more secondary motions associated with the one or more objects. || 11. One or more non-transitory computer-readable media containing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: receiving one or more Gaussian primitives representing a 3D scene including one or more objects; receiving one or more control inputs describing one or more primary motions associated with the one or more objects; generating, via a first trained machine learning model, a dynamic state based on the one or more control inputs; generating, via a second trained machine learning model, one or more deformed Gaussian primitives based on the dynamic state and the one or more Gaussian primitives; generating, via a renderer, a 2D representation of the 3D scene based on the one or more deformed Gaussian primitives and a virtual camera viewpoint; and generating an output sequence based at least on the 2D representation, wherein the output sequence depicts both the one or more primary motions associated with the one or more objects and one or more secondary motions associated with the one or more objects. || 19. A system comprising: one or more memories storing instructions; and one or more processors for executing the instructions to: receive one or more Gaussian primitives representing a 3D scene including one or more objects; receive one or more control inputs describing one or more primary motions associated with the one or more objects; generate, via a first trained machine learning model, a dynamic state based on the one or more control inputs; generate, via a second trained machine learning model, one or more deformed Gaussian primitives based on the dynamic state and the one or more Gaussian primitives; generate, via a renderer, a 2D representation of the 3D scene based on the one or more deformed Gaussian primitives and a virtual camera viewpoint; and generate an output sequence based at least on the 2D representation, wherein the output sequence depicts both the one or more primary motions associated with the one or more objects and one or more secondary motions associated with the one or more objects.