Attribute Transfer Between Polygonal Mesh Models
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Solution Overview
Problem
Existing computer animation methods face inefficiencies in transferring attributes between models due to differences in shape and topological connectivity, leading to failed attribute transfers and manual recreation of models, which is labor-intensive and costly.
Innovation Solution
The development of algorithms and systems that create compressed graphs of polygonal meshes, automate anchor vertex identification, and use lazy-greedy algorithms for approximate topological matching to facilitate attribute transfer between models with similar connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If attribute transfer algorithms are used between models with different shapes or topological connectivity, then attribute transfer should be performed, but the transfer fails or terminates without completing
Solution Approach 1:
The patent segments the polygonal meshes into compressed graph representations that capture topological connectivity independently of geometric shape. By converting meshes into graphs where vertices represent mesh elements and edges represent connectivity relationships, the system can identify isomorphic regions between models with different shapes but similar connectivity patterns, enabling successful attribute transfer where traditional shape-based methods would fail.
Solution Approach 2:
The patent changes the parameter basis for comparison from geometric attributes (shape, size, position) to topological attributes (connectivity, adjacency relationships, graph isomorphism). This parameter transformation allows the attribute transfer system to recognize similarity between models based on their connectivity structure rather than their visual appearance, resolving the contradiction between transferring attributes across different shapes and maintaining transfer reliability.
2Manufacturing precision
If models are manually recreated to fix attribute transfer failures, then correct attributes can be obtained, but labor and time are significantly increased
Solution Approach 1:
The patent implements self-service by providing automated feedback to animators about where attribute transfer fails and why. The system identifies non-isomorphic regions and communicates specific location and cause of failure, enabling animators to make targeted manual corrections only where needed rather than recreating entire models. This self-diagnostic capability reduces both time loss and labor while maintaining attribute accuracy.
3Measurement precision
If detailed comparison of all mesh faces is performed to identify differences, then precise difference locations can be found, but processing time and computational resources are greatly increased
Solution Approach 1:
The patent extracts the essential topological connectivity information from detailed mesh geometry to create compressed graph representations. By taking out only the connectivity relationships (which determine isomorphism) rather than analyzing every geometric detail of all mesh faces, the system achieves precise difference detection at a fraction of the computational cost. The graph abstraction preserves topological equivalence information while eliminating redundant geometric data.
Solution Approach 2:
The patent creates simplified copy representations (compressed graphs) of the original detailed meshes. These graph copies retain the topological connectivity information necessary for isomorphism detection while being much smaller and faster to process than the full mesh data. By working with these compressed copies for comparison and only applying results to the original detailed models when needed, the system achieves accurate difference detection with minimal processing time.
Data Source
AI summary
A method for automatically transferring attributes between computer-generated models. The method includes storing in memory first and second models represented by polygonal meshes and storing a set of attributes for the first model. A processor operates or runs a compressed graph generator to process the first and second models to generate first and second compressed graphs that are compressed versions of the models. The method includes comparing topological connectivity of the first and second compressed graphs. When the connectivity is similar, the method includes transferring at least a portion of the attributes from the first model to the second model. The compressed graphs may be motorcycle graphs, skeleton graphs, or other forms of compressed graphs. The method includes determining a pair of vertices in the first compressed graph that match vertices in the second compressed graph for use as starting locations in comparing topological connectivity of the compressed graphs.