ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant
  Tightness
Authors: name: Boqian Li, name: Haiwen Feng, name: Zeyu Cai, name: Michael J. Black, name: Yuliang Xiu

Fitting a body to a 3D clothed human point cloud is a common yet challenging
task. Traditional optimization-based approaches use multi-stage pipelines that
are sensitive to pose initialization, while recent learning-based methods often
struggle with generalization across diverse poses and garment types. We propose
Equivariant Tightness Fitting for Clothed Humans, or ETCH, a novel pipeline
that estimates cloth-to-body surface mapping through locally approximate SE(3)
equivariance, encoding tightness as displacement vectors from the cloth surface
to the underlying body. Following this mapping, pose-invariant body features
regress sparse body markers, simplifying clothed human fitting into an
inner-body marker fitting task. Extensive experiments on CAPE and 4D-Dress show
that ETCH significantly outperforms state-of-the-art methods -- both
tightness-agnostic and tightness-aware -- in body fitting accuracy on loose
clothing (16.7% ~ 69.5%) and shape accuracy (average 49.9%). Our equivariant
tightness design can even reduce directional errors by (67.2% ~ 89.8%) in
one-shot (or out-of-distribution) settings. Qualitative results demonstrate
strong generalization of ETCH, regardless of challenging poses, unseen shapes,
loose clothing, and non-rigid dynamics. We will release the code and models
soon for research purposes at https://boqian-li.github.io/ETCH/.