Application (pre-grant publication)
ANATOMICALLY CONSTRAINED IMPLICIT SHAPE MODELS
- Number
- 20250037366
- Published
- 2025-01-30
- Filed
- 2024-07-22
- Assignee
- DISNEY ENTERPRISES, INC.
- Inventors
- ZOSS; Gaspard et al.
- CPC
- G06T19/20; G06T17/00
- Verdict
- Low Notable software
- Source
- Google Patents · FreePatentsOnline
The keeper's note
Anatomically constrained implicit character shape-modeling technique.
Abstract
One embodiment of the present invention sets forth a technique for generating a shape model. The technique includes generating, via execution of a set of neural networks based on a plurality of shapes associated with an object, a set of attributes associated with a set of anatomical constraints for the object. The technique also includes computing, based on the set of attributes, a set of positions of a set of points on the object. The technique further includes generating a three-dimensional (3D) model of the object based on the set of positions of the set of points.
Background
BACKGROUND Field of the Various Embodiments
Embodiments of the present disclosure relate generally to machine learning and computer vision and, more specifically, to anatomically constrained implicit shape models. Description of the Related Art
Realistic digital representations of faces, hands, bodies, and other recognizable objects are required for various computer graphics and computer vision applications. For example, digital representations of real-world deformable objects may be used in virtual scenes of film or television productions, video games, virtual worlds, and/or other environments and/or settings.
One technique for representing a digital shape involves using a data-driven parametric shape model to characterize realistic variations in the appearance of the shape. The data-driven parametric shape model is typically built from a dataset of scans of the same type of shape and represents a new shape as a combination of existing shapes in the dataset.
One common parametric shape model includes a linear three-dimensional (3D) morphable model (3DMM) that expresses new faces, bodies, and/or other shapes as linear combinations of prototypical basis shapes from a dataset. However, the linear 3D morphable model is unable to represent continuous, nonlinear deformations that are common to faces and other recognizable shapes. At the same time, linear combinations of input shapes generated by the linear 3D morphable model can lead to unrealistic moti