TECHNIQUES FOR GENERATING A GENERALIZED PHYSICAL FACE MODEL

Number: 20250238992
Published: 2025-07-24
Filed: 2025-01-21
Assignee: DISNEY ENTERPRISES, INC.
Inventors: BRADLEY; Derek Edward et al.
CPC: G06T17/20; G06T13/40
Verdict: Low Notable software
Source: Google Patents · FreePatentsOnline

The keeper's note

Generalized physical facial-modeling technique for character rendering.

Abstract

The present invention sets forth techniques for generating a facial animation. The techniques include receiving a latent identity code including a first set of features describing a neutral facial depiction associated with an identity and receiving a latent expression code including a second set of features describing a facial expression associated with the identity. The techniques also include generating, via a first machine learning model, an identity-specific facial representation based on a canonical facial representation and the latent identity code and generating, via a second machine learning model and based on the latent identity code, the latent expression code, and the identity-specific facial representation, a muscle actuation field tensor and one or more bone transformations associated with the deformed canonical facial representation. The techniques further include generating, via a physics-based simulator, a facial animation based on at least the muscle actuation field tensor and the one or more bone transformations.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to 3D computer modeling and animation and, more specifically, to techniques for generating a generalized physical face model. Description of the Related Art

In the field of 3D computer animation, physics-based animation models, such as face models, provide artists with animation rigs that realistically obey physical properties. For example, physics-based face models may respond to external forces such as gravity, detect and avoid collisions between physical features included in the model, and respect anatomical structures, such as bone, skin, and tissue volume.

One existing technique for generating physics-based face models includes manually constructing a separate, person-specific model for each character to be animated. These techniques include defining a facial anatomy and building a physics-ready volumetric simulation mesh representing the soft tissues included in the face model. Manual construction is time-consuming, and may require several iterative attempts by one or more artists to generate a satisfactory physics-based face model. Further, a person-specific model must be subsequently augmented with person-specific muscle actuations that will result in desired facial expressions. These actuations must also be parameterized by an artist-friendly rig space, e.g., blend shapes. Accordingly, these manual techniques may be limited to generating physics-bas

Claims

1. A computer-implemented method for generating a facial animation, the computer-implemented method comprising: receiving an identity code including a first set of features describing a neutral facial depiction associated with a particular identity; receiving an expression code including a second set of features describing a facial expression associated with the particular identity; generating, via a first machine learning model, an identity-specific facial representation based on a canonical facial representation and the identity code; generating, via a second machine learning model and based on the identity code, the expression code, and the identity-specific facial representation, a muscle actuation field tensor and one or more bone transformations associated with the canonical facial representation; and generating, via a physics-based simulator, a facial animation based on at least the muscle actuation field tensor and the one or more bone transformations. || 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 an identity code including a first set of features describing a neutral facial depiction associated with a particular identity; receiving an expression code including a second set of features describing a facial expression associated with the particular identity; generating, via a first machine learning model, an identity-specific facial representation based on a canonical facial representation and the identity code; generating, via a second machine learning model and based on the identity code, the expression code, and the identity-specific facial representation, a muscle actuation field tensor and one or more bone transformations associated with the canonical facial representation; and generating, via a physics-based simulator, a facial animation based on at least the muscle actuation field tensor and the one or more bone transformations. || 19. A system comprising: one or more memories storing instructions; and one or more processors for executing the instructions to: receive an identity code including a first set of features describing a neutral facial depiction associated with a particular identity; receive an expression code including a second set of features describing a facial expression associated with the particular identity; generate, via a first machine learning model, an identity-specific facial representation based on a canonical facial representation and the identity code; generate, via a second machine learning model and based on the identity code, the expression code, and the identity-specific facial representation, a muscle actuation field tensor and one or more bone transformations associated with the canonical facial representation; and generate, via a physics-based simulator, a facial animation based on at least the muscle actuation field tensor and the one or more bone transformations.