ScanMove: Motion Prediction and Transfer for Unregistered Body Meshes
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912558022131712 |
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| author | Besnier, Thomas Arguillère, Sylvain Daoudi, Mohamed |
| author_facet | Besnier, Thomas Arguillère, Sylvain Daoudi, Mohamed |
| contents | Unregistered surface meshes, especially raw 3D scans, present significant challenges for automatic computation of plausible deformations due to the lack of established point-wise correspondences and the presence of noise in the data. In this paper, we propose a new, rig-free, data-driven framework for motion prediction and transfer on such body meshes. Our method couples a robust motion embedding network with a learned per-vertex feature field to generate a spatio-temporal deformation field, which drives the mesh deformation. Extensive evaluations, including quantitative benchmarks and qualitative visuals on tasks such as walking and running, demonstrate the effectiveness and versatility of our approach on challenging unregistered meshes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21095 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ScanMove: Motion Prediction and Transfer for Unregistered Body Meshes Besnier, Thomas Arguillère, Sylvain Daoudi, Mohamed Graphics Computer Vision and Pattern Recognition Unregistered surface meshes, especially raw 3D scans, present significant challenges for automatic computation of plausible deformations due to the lack of established point-wise correspondences and the presence of noise in the data. In this paper, we propose a new, rig-free, data-driven framework for motion prediction and transfer on such body meshes. Our method couples a robust motion embedding network with a learned per-vertex feature field to generate a spatio-temporal deformation field, which drives the mesh deformation. Extensive evaluations, including quantitative benchmarks and qualitative visuals on tasks such as walking and running, demonstrate the effectiveness and versatility of our approach on challenging unregistered meshes. |
| title | ScanMove: Motion Prediction and Transfer for Unregistered Body Meshes |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.21095 |