Outer Rim Archives
Archives · 2026 · 20260203984

Application (pre-grant publication)

NEURAL SHAPE DEFORMATION TRANSFER

Number
20260203984
Published
2026-07-16
Filed
2025-01-13
Assignee
DISNEY ENTERPRISES, INC.
Inventors
BRADLEY; Derek Edward, CICCONE; Loïc Florian, ZOSS; Gaspard, CHANDRAN; Prashanth
CPC
G06T13/40; G06T17/00; G06T19/20
Verdict
Low Notable software
First reported
2026-W29 (2026-07-16)
Source
Google Patents · FreePatentsOnline

The keeper's note

One embodiment of the present invention sets forth a technique for generating a shape.

Abstract

One embodiment of the present invention sets forth a technique for generating a shape. The technique includes determining (i) a deformed template shape corresponding to a non-neutral expression on a template subject and (ii) a neutral target shape corresponding to a neutral expression on a target subject. The technique also includes generating input representing the deformed template shape and the neutral target shape. The technique further includes generating, via execution of a machine learning model based on the input, a deformed target shape corresponding to the non-neutral expression on the target subject.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to machine learning and computer vision and, more specifically, to neural shape deformation transfer. Description of the Related Art

Blendshape generation refers to a process of creating a set of blendshapes that include deformations of a “baseline” shape. For example, a set of blendshapes for a face may include different facial expressions made using the face. After the set of blendshapes is created, the blendshapes can be linearly combined via corresponding blendshape coefficients (also known as blendweights) to generate new deformations and/or animations of the face.

Traditionally, blendshape generation typically involves significant time and/or resource overhead. For example, a set of blendshapes for an actor may be generated by scanning the face of the actor using specialized equipment while the actor performs a series of predefined facial expressions. In another example, an artist may use computer-based tools to manually sculpt hundreds of three-dimensional (3D) meshes corresponding to a range of realistic expressions for a virtual character. This set of meshes may be iteratively refined to add detail to and/or adjust the appearance of the virtual character, thereby consuming additional time and resources (e.g., multiple months to a year).

More recently, techniques have been developed to transfer a set of blendshapes corresponding to deformation

Claims

1. A computer-implemented method for generating a shape, the method comprising: determining (i) a deformed template shape corresponding to a non-neutral expression on a template subject and (ii) a neutral target shape corresponding to a neutral expression on a target subject; generating input representing the deformed template shape and the neutral target shape; and generating, via execution of a machine learning model based on the input, a deformed target shape corresponding to the non-neutral expression on the target subject. || 11. 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: determining (i) a deformed template shape corresponding to a first non-neutral expression on a template subject and (ii) a neutral target shape corresponding to a neutral expression on a target subject; generating input representing the deformed template shape and the neutral target shape; and generating, via execution of a trained machine learning model based on the input, a deformed target shape corresponding to the first non-neutral expression on the target subject. || 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: determining (i) a deformed template shape corresponding to a non-neutral expression on a template subject and (ii) a neutral target shape corresponding to a neutral expression on a target subject; generating input representing the deformed template shape and the neutral target shape; and generating, via execution of a machine learning model based on the input, a deformed target shape corresponding to the non-neutral expression on the target subject.