- Number
- 12749250
- Published
- 2026-09-29
- Filed
- 2024-07-10
- Assignee
- Disney Enterprises, INC.
- Inventors
- Guay; Martin, Agrawal; Dhruv, Borer; Dominik Tobias, Buhmann; Jakob Joachim, Sumner; Robert Walker
- CPC
- G06T13/40; G06F17/16; G06N3/0455; G06N3/0475; G06N3/09
- Verdict
- High Notable software
- First reported
- 2026-W40 (2026-10-02)
- Source
- Google Patents · FreePatentsOnline
The keeper's note
Uses a neural network to solve a virtual character's joint poses from constraints — inverse-kinematics tech relevant to robotics/animatronic motion control.
Abstract
One embodiment of the present invention sets forth a technique for generating a pose for a virtual character. The technique includes determining a set of joint representations corresponding to a set of joints in the virtual character based on (i) a base pose for the virtual character and (ii) a set of constraints associated with one or more joints included in the set of joints. The technique also includes generating, via execution of a first neural network, a set of updated joint states for the set of joints based on the set of joint representations. The technique further includes generating, based on the set of updated joint states, an output pose that includes (i) a first set of joint positions for the set of joints and (ii) a first set of joint orientations for the set of joints.
Background
BACKGROUND Field of the Various Embodiments (1) Embodiments of the present disclosure relate generally to computer vision and machine learning and, more specifically, to pose-aware neural inverse kinematics. Description of the Related Art (2) Films, video games, virtual reality (VR) systems, augmented reality (AR) systems, mixed reality (MR) systems, robotics, and/or other types of interactive environments frequently include entities (e.g., characters, robots, etc.) that are posed and/or animated in three-dimensional (3D) space. Traditional techniques for posing an entity involve manually manipulating multiple control handles corresponding to joints (or other parts) of the entity. An inverse kinematics (IK) technique can also be used to compute the positions and orientations of remaining joints (or parts) of the entity that result in the desired configuration of the manipulated joints (or parts). (3) However, posing and/or animating entities using conventional IK techniques is associated with a number of drawbacks. First, posing an entity via manipulation of control handles is a time-consuming, iterative, and laborious process. This manual process is repeated for a sequence of poses corresponding to movements that are used to animate the entity, which incurs additional time and resource overhead. Second, poses generated via traditional IK techniques can be unnatural or unrealistic. (4) Recent advancements in machine learning and deep learning have led to the development of ne
Claims
1. A computer-implemented method for generating a pose for a virtual character, comprising: determining a set of joint representations corresponding to a set of joints in the virtual character based on (i) a base pose associated with the set of joints, (ii) a set of constraints that include changes to the base pose for one or more joints included in the set of joints, and (iii) a set of control parameters; generating, via execution of a first neural network, a set of updated joint states for the set of joints based on the set of joint representations; and generating, based on the set of updated joint states, an output pose that includes (i) a first set of joint positions for the set of joints and (ii) a first set of joint orientations for the set of joints, wherein an influence of the base pose and an additional influence of the set of constraints on the first set of joint positions and the first set of joint orientations is determined based on the set of control parameters. ||
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 operations comprising: determining a set of joint representations corresponding to a set of joints in a virtual character based on (i) a base pose associated with the set of joints, (ii) a set of constraints that include changes to the base pose for one or more joints included in the set of joints, and (iii) a set of control parameters; generating, via execution of a first neural network, a set of updated joint states for the set of joints based on the set of joint representations; and generating, based on the set of updated joint states, an output pose that includes (i) a first set of joint positions for the set of joints and (ii) a first set of joint orientations for the set of joints, wherein an influence of the base pose and an additional influence of the set of constraints on the first set of joint positions and the first set of joint orientations is determined based on the set of control parameters. ||
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 operations comprising: determining a set of joint representations corresponding to a set of joints in a virtual character based on (i) a base pose associated with the set of joints, (ii) a set of constraints that include changes to the base pose for one or more joints included in the set of joints, and (iii) a set of control parameters; generating, via execution of a first neural network, a set of updated joint states for the set of joints based on the set of joint representations; and generating, based on the set of updated joint states, an output pose that includes (i) a first set of joint positions for the set of joints and (ii) a first set of joint orientations for the set of joints, wherein an influence of the base pose and an additional influence of the set of constraints on the first set of joint positions and the first set of joint orientations is determined based on the set of control parameters.