Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondence
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909741023756288 |
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| author | Chen, Ling-Hao Zhang, Yuhong Yin, Zixin Dou, Zhiyang Chen, Xin Wang, Jingbo Komura, Taku Zhang, Lei |
| author_facet | Chen, Ling-Hao Zhang, Yuhong Yin, Zixin Dou, Zhiyang Chen, Xin Wang, Jingbo Komura, Taku Zhang, Lei |
| contents | This work studies the challenge of transfer animations between characters whose skeletal topologies differ substantially. While many techniques have advanced retargeting techniques in decades, transfer motions across diverse topologies remains less-explored. The primary obstacle lies in the inherent topological inconsistency between source and target skeletons, which restricts the establishment of straightforward one-to-one bone correspondences. Besides, the current lack of large-scale paired motion datasets spanning different topological structures severely constrains the development of data-driven approaches. To address these limitations, we introduce Motion2Motion, a novel, training-free framework. Simply yet effectively, Motion2Motion works with only one or a few example motions on the target skeleton, by accessing a sparse set of bone correspondences between the source and target skeletons. Through comprehensive qualitative and quantitative evaluations, we demonstrate that Motion2Motion achieves efficient and reliable performance in both similar-skeleton and cross-species skeleton transfer scenarios. The practical utility of our approach is further evidenced by its successful integration in downstream applications and user interfaces, highlighting its potential for industrial applications. Code and data are available at https://lhchen.top/Motion2Motion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13139 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondence Chen, Ling-Hao Zhang, Yuhong Yin, Zixin Dou, Zhiyang Chen, Xin Wang, Jingbo Komura, Taku Zhang, Lei Computer Vision and Pattern Recognition This work studies the challenge of transfer animations between characters whose skeletal topologies differ substantially. While many techniques have advanced retargeting techniques in decades, transfer motions across diverse topologies remains less-explored. The primary obstacle lies in the inherent topological inconsistency between source and target skeletons, which restricts the establishment of straightforward one-to-one bone correspondences. Besides, the current lack of large-scale paired motion datasets spanning different topological structures severely constrains the development of data-driven approaches. To address these limitations, we introduce Motion2Motion, a novel, training-free framework. Simply yet effectively, Motion2Motion works with only one or a few example motions on the target skeleton, by accessing a sparse set of bone correspondences between the source and target skeletons. Through comprehensive qualitative and quantitative evaluations, we demonstrate that Motion2Motion achieves efficient and reliable performance in both similar-skeleton and cross-species skeleton transfer scenarios. The practical utility of our approach is further evidenced by its successful integration in downstream applications and user interfaces, highlighting its potential for industrial applications. Code and data are available at https://lhchen.top/Motion2Motion. |
| title | Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondence |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.13139 |