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.

使用扩散模型学习的形状先验和基于学习的功能映射的多阶段引导方案，实现了纹理不可见衣物的准确表面配准，对于大规模变形的稳定配准效果优于基于 VAE 或 PCA 先验的表面配准，并且在插值和外推测试中优于基于优化和学习的非刚性配准方法。

用于准确对位褶皱布料的扩散形状先验

Diffusion Shape Prior for Wrinkle-Accurate Cloth Registration

Appearance of dressed humans undergoes a complex geometric transformation
induced not only by the static pose but also by its dynamics, i.e., there
exists a number of cloth geometric configurations given a pose depending on the
way it has moved. Such appearance modeling conditioned on motion has been
largely neglected in existing human rendering methods, resulting in rendering
of physically implausible motion. A key challenge of learning the dynamics of
the appearance lies in the requirement of a prohibitively large amount of
observations. In this paper, we present a compact motion representation by
enforcing equivariance -- a representation is expected to be transformed in the
way that the pose is transformed. We model an equivariant encoder that can
generate the generalizable representation from the spatial and temporal
derivatives of the 3D body surface. This learned representation is decoded by a
compositional multi-task decoder that renders high fidelity time-varying
appearance. Our experiments show that our method can generate a temporally
coherent video of dynamic humans for unseen body poses and novel views given a
single view video.

本研究提出一种新的具有运动条件的人类动态外观模型，使用等变编码器从三维身体表面的空间和时间导数生成可推广的表征，再通过组合式多任务解码器渲染具有高保真度的时间变化外观的视频，实验结果表明，本方法能生成视角全新的人类动态视频。