点级学习变形，实现3D网格局部精准控制。
PaNDaS: Learnable Deformation Modeling with Localized Control
- 在点级别学习变形，支持局部区域灵活操控。
- 重建与插值精度优于全局表示方法，局部性更强。
- 无需推理优化，可组合多种姿态生成新形状。
非刚性形变建模面临重大挑战，现有方法难以有效处理部分形变。本文提出点级变形学习框架（PaNDaS），可在3D表面网格上实现局部化控制，支持部分非刚性形变与表面插值。与以往方法不同，该方法能以灵活方式将形变限制在特定区域。此外，用户可自由混合数据库中的多种姿态，且推理时无需优化。在各类人体表面数据上，该方法在形变重建与插值任务中均达到当前最优精度，并展现出更强的局部性。我们还展示了多项局部形变操作能力，证明其可通过组合不同输入形变生成新形状。代码与数据将在评审后公开。
原文摘要 · Abstract (English)
Non-rigid shape deformations pose significant challenges, and most existing methods struggle to handle partial deformations effectively. We propose to learn deformations at the point level, which allows for localized control of 3D surface meshes, enabling Partial Non-rigid Deformations and interpolations of Surfaces (PaNDaS). Unlike previous approaches, our method can restrict the deformations to specific parts of the shape in a versatile way. Moreover, one can mix and combine various poses from the database, all while not requiring any optimization at inference time. We demonstrate state-of-the-art accuracy and greater locality for shape reconstruction and interpolation compared to approaches relying on global shape representation across various types of human surface data. We also demonstrate several localized shape manipulation tasks and show that our method can generate new shapes by combining different input deformations. Code and data will be made available after the reviewing process.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。