Non-rigid shape deformations pose significant challenges, and most existing methods struggle to handle partial deformations effectively. We present Partial Non-rigid Deformations and interpolations of the human body Surfaces (PaNDAS), a new method to learn local and global deformations of 3D surface meshes by building on recent deep models. Unlike previous approaches, our method enables restricting deformations to specific parts of the shape in a versatile way and allows for mixing and combining various poses from the database, all while not requiring any optimization at inference time. We demonstrate that the proposed framework can be used to generate new shapes, interpolate between parts of shapes, and perform other shape manipulation tasks with state-of-the-art accuracy and greater locality across various types of human surface data. Code and data will be made available soon.

翻译：非刚性形状形变带来了重大挑战，现有方法大多难以有效处理局部形变。本文提出人体表面局部非刚性形变与插值方法（PaNDAS），这是一种基于近期深度学习模型、用于学习三维表面网格局部与全局形变的新方法。与先前方法不同，我们的方法能够以灵活的方式将形变限制在形状的特定区域，并支持对数据库中多种姿态进行混合与组合，且在推理过程中无需任何优化。实验表明，所提框架可用于生成新形状、在形状局部之间进行插值，以及执行其他形状编辑任务，在各类人体表面数据上均实现了最先进的精度与更强的局部控制能力。代码与数据即将公开。