ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant
  Tightness
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/.
67d5b604f58a6a411a5bb598
Boqian Li
claimed_verified
2025-03-16T21:12:23.978Z
65987383bf533e3c0dd1914b
/avatars/4b3c0e791ef24a439a43371c4d92bc4b.svg
Boqian Li
user
Boqian-Li
67d5b604f58a6a411a5bb599
Haiwen Feng
67d5b604f58a6a411a5bb59a
Zeyu Cai
67d5b604f58a6a411a5bb59b
Michael J. Black
67d5b604f58a6a411a5bb59c
Yuliang Xiu
claimed_verified
2025-05-12T12:56:28.526Z
6183d0b249ef1d984699e4a3
https://cdn-avatars.huggingface.co/v1/production/uploads/6183d0b249ef1d984699e4a3/M6B9b73v8su9H8jfdcbgu.jpeg
Yuliang Xiu
user
Yuliang
ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant
  Tightness
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/.
ETCH improves body fitting in 3D point clouds by estimating cloth-to-body mapping using locally approximate SE(3) equivariance, outperforming state-of-the-art methods in accuracy and generalization.
SE(3) equivariance tightness-aware tightness-agnostic body fitting 3D point clouds CAPE 4D-Dress body markers inner-body marker fitting generalization directional errors non-rigid dynamics