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Tiling Motion Patches
Motion synthesis approach using tiled motion patches for seamlessly transitioning and looping character locomotion in interactive applications.
Abstract
Proposes a tiling algorithm for creating dense crowds of interacting virtual characters using deformable motion patches. The method collects episodes of multiple characters and tiles them spatially and temporally to generate seamless multi-character animation with complex interactions like hand shaking and object carrying, using a combination of stochastic sampling and deterministic search.
How to read this
- Category
- Method: multi-character motion synthesis
- Contributions
- Tiles deformable motion patches spatially and temporally to synthesize dense crowds of interacting characters
- Collects episodes of multiple characters and assembles them into seamless multi-character animation
- Combines stochastic sampling with deterministic search to generate complex interactions such as hand shaking and object carrying
- Context
- Builds on the motion-graph lineage (Kovar et al. Motion Graphs), extending data-driven reassembly from single-character paths to tiled multi-character interaction patches.Builds on: Motion Graphs
- Correctness
- Demonstrated on crowds with close interactions; the result space is bounded by the captured episodes and the tileability of patches, so interactions outside the recorded repertoire will not appear and seam quality depends on patch compatibility.
- Clarity
- Accessible; a first pass conveys the tiling metaphor, a second pass clarifies the sampling-plus-search synthesis loop.
- How to read it
- Focus on what a motion patch is and how tiles compose without seams; a second pass on the sampling/search algorithm is worth it if you are building interactive crowds.
Builds on
- Motion Graphs 2002
Built upon by
Nothing yet.
Related work
- Near-Optimal Character Animation with Continuous Control 2007 / SIGGRAPH
- DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds 2022 / SIGGRAPH
- Physics-Based Character Controllers Using Conditional VAEs 2022 / SIGGRAPH
- Automated Extraction and Parameterization of Motions in Large Data Sets 2004 / SIGGRAPH
Keywords
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