FACTORIZED MOTION COMPLETION FOR PRECISE AND CHARACTER-AGNOSTIC MOTION DIFFUSION

Number: 20260141607
Published: 2026-05-21
Filed: 2024-11-20
Assignee: DISNEY ENTERPRISES, INC.
Inventors: BUHMANN; Jakob Joachim, STUDER; Justin Thierry, BORER; Dominik Tobias, AGRAWAL; Dhruv, GUAY; Martin, SUMNER; Robert Walker
CPC: G06T13/40; G06T2200/04
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 animating a three-dimensional (3D) character model.

Abstract

The present invention sets forth techniques for animating a three-dimensional (3D) character model. The disclosed techniques include receiving one or more spatial constraints associated with a first set of one or more reference points included in a first 3D character model, and generating, via a first machine learning model and based on the one or more spatial constraints, a set of trajectories associated with each reference point included in the first set of one or more reference points. The techniques also include generating, via a second machine learning model and based on the set of trajectories, a set of framewise positions associated with a second set of one or more reference points included in a second 3D character model. The techniques further include generating an output animation depicting motion of the second 3D character model based on the set of framewise positions.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to computer animation and, more specifically, to techniques for animating a three-dimensional (3D) character via diffusion models. Description of the Related Art

Animating a 3D character model including one or more bones, joints, or other reference points is a common task in the field of computer animation. While 3D character motion may be modeled via explicitly defining the positions over time of one or more joints, bones, or reference points included in the 3D character model, these manual animation techniques are cumbersome and time-consuming. Neural motion completion techniques attempt to at least partially automate 3D character animation by predicting a sequence of character motions based on a starting position, previous motions, and/or one or more conditioning inputs.

Existing techniques for automating 3D character animation may include one or more motion diffusion models. These generative machine learning models attempt to produce realistic 3D character model movements, and may be conditioned on, ec, text inputs or sample movements. One drawback to these existing systems is that the existing motion diffusion models may be trained on a specific 3D character model, and may not be suitable for use with a different 3D character model. Further, existing motion diffusion models may not be operable to infer an animation sequence that is conditioned on a rela

Claims

1. A computer-implemented method for animating a three-dimensional (3D) character model, the computer-implemented method comprising: receiving one or more spatial constraints associated with a first set of one or more reference points included in a first 3D character model; generating, via a first machine learning model and based on the one or more spatial constraints, a set of trajectories associated with each reference point included in the first set of one or more reference points; generating, via a second machine learning model and based on the set of trajectories, a set of framewise positions associated with a second set of one or more reference points included in a second 3D character model; and generating an output animation depicting motion of the second 3D character model based on the set of framewise positions. || 10. 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 one or more spatial constraints associated with a first set of one or more reference points included in a first 3D character model; generating, via a first machine learning model and based on the one or more spatial constraints, a set of trajectories associated with each reference point included in the first set of one or more reference points; generating, via a second machine learning model and based on the set of trajectories, a set of framewise positions associated with a second set of one or more reference points included in a second 3D character model; and generating an output animation depicting motion of the second 3D character model based on the set of framewise positions. || 19. A system comprising: one or more memories storing instructions; and one or more processors for executing the instructions to: receive one or more spatial constraints associated with a first set of one or more reference points included in a first 3D character model; generate, via a first machine learning model and based on the one or more spatial constraints, a set of trajectories associated with each reference point included in the first set of one or more reference points; generate, via a second machine learning model and based on the set of trajectories, a set of framewise positions associated with a second set of one or more reference points included in a second 3D character model; and generate an output animation depicting motion of the second 3D character model based on the set of framewise positions.