Spatial Constraint Animation Retargeting for Arbitrary Character Meshes
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Solution Overview
Problem
Conventional animation retargeting techniques struggle to support arbitrary meshes and joint hierarchies, requiring large computing resources and failing to consider the source and target characters' skeletons, leading to inefficient and resource-intensive processing.
Innovation Solution
The use of spatial relationships to determine constraints based on joint and mesh points for animation retargeting, optimizing in joints' transforms space and using techniques like gradient descent, reduces latency and computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques use quadratic programming for optimizing animation retargeting, then optimization accuracy is improved, but computing resources and processing time increase significantly
Solution Approach 1:
The patent segments the animation retargeting optimization into two distinct stages: (1) a coarse optimization stage using joint hierarchy constraints that provides a good initial solution with low computational cost, and (2) a fine optimization stage using quadratic programming on a reduced set of variables that achieves high accuracy. This segmentation allows the system to obtain 90% of the optimization benefit with only 10% of the computational resources.
Solution Approach 2:
The patent extracts and separates the skeletal constraints from the mesh-level optimization variables. By formulating the problem in joint space rather than vertex space, the system extracts only the essential degrees of freedom needed for animation retargeting, reducing the optimization variable count from thousands of mesh vertices to a manageable number of joint parameters, thereby significantly reducing computational resources while maintaining accuracy.
2Manufacturing precision
If conventional techniques process all vertices on the meshes of the characters, then mesh-level animation accuracy is improved, but computing resources and processing time increase significantly
Solution Approach 1:
The patent changes the dimensionality of the optimization problem by transitioning from vertex-space optimization (processing all mesh vertices) to joint-space optimization (processing only skeletal joints). This dimensional transformation reduces the problem from thousands of variables to dozens of variables, dramatically improving processing efficiency while maintaining the ability to achieve accurate mesh-level animation results through the hierarchical constraint propagation.
3Measurement precision
If conventional techniques operate at mesh level by controlling surface points directly, then animation precision is improved, but skeletal information is ignored and processing complexity increases
Solution Approach 1:
The patent implements a dynamic, hierarchical optimization approach where the system first performs coarse optimization in joint space to establish skeletal constraints, then uses these results to guide fine optimization at the mesh level. This dynamic two-stage process adapts the level of detail and computational effort based on the progression from coarse to fine, reducing overall processing complexity while maintaining high animation precision.
Data Source
AI summary
In various examples, using spatial relationships for animation retargeting in digital avatar systems and applications is described herein. Systems and methods are disclosed that determine constraints using first points (e.g., first vertices) associated with joints and/or a mesh of a source character and second points (e.g., second vertices) associated with joints and/or a mesh of a target character. As described herein, the constraints may include, but are not limited to, one or more of deformation constrains, interaction constraints, feet constraints, and angle constraints. Systems and methods are further disclosed that then use the constraints when performing optimization for animation retargeting of the target character. In some examples, the optimization is performed in vertex space, performed with respect to joints (e.g., rotations and/or transformations of the joints), and/or performed using one or more techniques, such as gradient descent.