mlsanityData to 14 Oct 2026

ScanMove: Motion Prediction and Transfer for Unregistered Body Meshes

arXiv 2508.21095 ↗First posted 27 Aug 2025cs.GR cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 35 of its 64 references that are in the map. First posted on arXiv 27 Aug 2025.

Builds on

35 of 64 references.

References · 35

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23 more
  1. 2019Neural Cages for Detail-Preserving 3D Deformationscited by 106
  2. 2019Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generationcited by 90
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  15. 2023Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wildcited by 25
  16. 2024ScanTalk: 3D Talking Heads from Unregistered Scanscited by 24
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  23. 2024Temporal Residual Jacobians For Rig-free Motion Transfercited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completioncited by 0