Registering clothes from 4D scans with vertex-accurate correspondence is challenging, yet important for dynamic appearance modeling and physics parameter estimation from real-world data. However, previous methods either rely on texture information, which is not always reliable, or achieve only coarse-level alignment. In this work, we present a novel approach to enabling accurate surface registration of texture-less clothes with large deformation. Our key idea is to effectively leverage a shape prior learned from pre-captured clothing using diffusion models. We also propose a multi-stage guidance scheme based on learned functional maps, which stabilizes registration for large-scale deformation even when they vary significantly from training data. Using high-fidelity real captured clothes, our experiments show that the proposed approach based on diffusion models generalizes better than surface registration with VAE or PCA-based priors, outperforming both optimization-based and learning-based non-rigid registration methods for both interpolation and extrapolation tests.

翻译：从4D扫描中实现顶点精确对应的服装配准具有挑战性，但对于动态外观建模和从真实数据中估计物理参数至关重要。然而，现有方法要么依赖并不可靠的纹理信息，要么仅能实现粗粒度对齐。本文提出一种新方法，可在无纹理且存在大形变的服装上实现精确表面配准。其核心思想是高效利用通过扩散模型从预采集服装中学习到的形状先验。我们还提出基于学习的功能映射的多阶段引导方案，即使形变与训练数据存在显著差异，也能稳定大规模形变的配准过程。使用高保真真实采集服装进行的实验表明，基于扩散模型的所提方法比基于VAE或PCA先验的表面配准具有更强的泛化能力，在插值和外推测试中均优于基于优化和非刚性配准的深度学习方法。