Application (pre-grant publication)
POSE-AWARE NEURAL INVERSE KINEMATICS
- Number
- 20250022202
- Published
- 2025-01-16
- Filed
- 2024-07-10
- Assignee
- DISNEY ENTERPRISES, INC.
- Inventors
- GUAY; Martin et al.
- CPC
- G06T13/40; G06F17/16; G06N3/042; G06N3/0455; G06N3/0475; G06N3/084; G06N3/09
- Verdict
- Low Notable software
- Source
- Google Patents · FreePatentsOnline
The keeper's note
Pose-aware neural inverse-kinematics character technique.
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
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
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).
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.
Recent advancements in machine learning and deep learning have led to the devel