- Number
- 20250209715
- Published
- 2025-06-26
- Filed
- 2024-12-10
- Assignee
- DISNEY ENTERPRISES, INC.
- Inventors
- GUAY; Martin et al.
- CPC
- G06N3/045; G06T13/40; G06N3/09; G06T13/80
- Verdict
- Low Notable software
- Source
- Google Patents · FreePatentsOnline
The keeper's note
Generalized character pose/motion generation 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 graph representation of one or more sets of joints in the virtual character based on (i) constraints associated with one or more joints included in the set(s) of joints and (ii) proportions associated with pairs of joints included in the set(s) of joints. The technique also includes generating, via execution of a neural network, a set of updated node states for the set(s) of joints based on the graph representation. The technique further includes generating, based on the updated node states, one or more output poses that correspond to the set(s) of joints, wherein the output pose(s) include (i) a first set of joint positions for the set(s) of joints, (ii) a first set of joint orientations for the set(s) of joints, and (iii) the proportions.
Background
BACKGROUND Field of the Various Embodiments
Embodiments of the present disclosure relate generally to computer vision and machine learning and, more specifically, to generalized pose and motion generation. 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. Traditionally, an entity is posed via a time-consuming, iterative, and laborious process of 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). To animate the entity, this manual process is repeated for additional keyframes within a sequence of poses representing movements of the entity, with poses for frames between keyframes generated by interpolating between the keyframes using parametric curves.
More recently, advancements in machine learning and deep learning have led to the development of neural IK models and/or neural motion completion models. The neural IK models include neural networks that leverage full-body correlations learned from large datasets to compute t
Claims
1. A computer-implemented method for generating a pose for a virtual character, comprising: determining a graph representation of one or more sets of joints in the virtual character based on (i) a set of constraints associated with one or more joints included in the one or more sets of joints and (ii) a set of proportions associated with pairs of joints included in the one or more sets of joints; generating, via execution of a first neural network, a set of updated node states for the one or more sets of joints based on the graph representation; and generating, based on the set of updated node states, one or more output poses that correspond to the one or more sets of joints, wherein the one or more output poses include (i) a first set of joint positions for the one or more sets of joints, (ii) a first set of joint orientations for the one or more sets of joints, and (iii) the set of proportions. ||
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 graph representation of one or more sets of joints in a virtual character based on (i) a set of constraints associated with one or more joints included in the one or more sets of joints and (ii) a set of proportions associated with pairs of joints included in the one or more sets of joints; generating, via execution of a first neural network, a set of updated node states for the one or more sets of joints based on the graph representation; and generating, based on the set of updated node states, one or more output poses that correspond to the one or more sets of joints, wherein the one or more output poses include (i) a first set of joint positions for the one or more sets of joints, (ii) a first set of joint orientations for the one or more sets of joints, and (iii) the set of proportions. ||
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 graph representation of one or more sets of joints in a virtual character based on (i) one or more input poses for the virtual character, (ii) a set of constraints associated with one or more joints included in the one or more sets of joints, (iii) a set of proportions associated with pairs of joints included in the one or more sets of joints, and (iv) a style associated with the virtual character; generating, via execution of a first neural network, a set of updated node states for the one or more sets of joints based on the graph representation; and generating, based on the set of updated node states, one or more output poses that correspond to the one or more sets of joints, wherein the one or more output poses include (i) a first set of joint positions for the one or more sets of joints, (ii) a first set of joint orientations for the one or more sets of joints, and (iii) the set of proportions.