Two new research papers submitted to arXiv on September 29, 2026, address the challenge of cross-skeleton motion retargeting. The first paper introduces a Transformer Autoencoder with a learnable flattening technique to improve zero-shot motion transfer between diverse skeletal structures, achieving significant error reduction and user-rated plausibility. The second paper, however, highlights the inherent non-identifiability of cross-skeleton motion generation, explaining that standard generative models struggle to distinguish between true motion transfer and typical motions for a given action, proposing a diagnostic tool called Source-Instance Fidelity (SIF) to better evaluate these methods. AI
IMPACT These papers advance research in generative motion models and skeletal animation, potentially improving realism and efficiency in animation and virtual reality applications.
RANK_REASON Two academic papers published on arXiv discussing motion retargeting.
- alphaXiv
- arXiv
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- CORE Recommender
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- Generative Motion Models
- Gotit.pub
- Hugging Face
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- ScienceCast
- Source-Instance Fidelity (SIF)
- Source-Skeleton Retargeting
- Transformer
- Transformer Autoencoder
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