  Learning to reconstruct 3D garments is important for dressing 3D human bodies
of different shapes in different poses. Previous works typically rely on 2D
images as input, which however suffer from the scale and pose ambiguities. To
circumvent the problems caused by 2D images, we propose a principled framework,
Garment4D, that uses 3D point cloud sequences of dressed humans for garment
reconstruction. Garment4D has three dedicated steps: sequential garments
registration, canonical garment estimation, and posed garment reconstruction.
The main challenges are two-fold: 1) effective 3D feature learning for fine
details, and 2) capture of garment dynamics caused by the interaction between
garments and the human body, especially for loose garments like skirts. To
unravel these problems, we introduce a novel Proposal-Guided Hierarchical
Feature Network and Iterative Graph Convolution Network, which integrate both
high-level semantic features and low-level geometric features for fine details
reconstruction. Furthermore, we propose a Temporal Transformer for smooth
garment motions capture. Unlike non-parametric methods, the reconstructed
garment meshes by our method are separable from the human body and have strong
interpretability, which is desirable for downstream tasks. As the first attempt
at this task, high-quality reconstruction results are qualitatively and
quantitatively illustrated through extensive experiments. Codes are available
at https://github.com/hongfz16/Garment4D.
