title: ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant
    Tightness
summary: 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/.
paper_authors._id: 67d5b604f58a6a411a5bb598
paper_authors.name: Boqian Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:12:23.978Z
paper_authors._id: 65987383bf533e3c0dd1914b
paper_authors.avatarUrl: /avatars/4b3c0e791ef24a439a43371c4d92bc4b.svg
paper_authors.fullname: Boqian Li
paper_authors.type: user
paper_authors.user: Boqian-Li
paper_authors._id: 67d5b604f58a6a411a5bb599
paper_authors.name: Haiwen Feng
paper_authors._id: 67d5b604f58a6a411a5bb59a
paper_authors.name: Zeyu Cai
paper_authors._id: 67d5b604f58a6a411a5bb59b
paper_authors.name: Michael J. Black
paper_authors._id: 67d5b604f58a6a411a5bb59c
paper_authors.name: Yuliang Xiu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-12T12:56:28.526Z
paper_authors._id: 6183d0b249ef1d984699e4a3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6183d0b249ef1d984699e4a3/M6B9b73v8su9H8jfdcbgu.jpeg
paper_authors.fullname: Yuliang Xiu
paper_authors.type: user
paper_authors.user: Yuliang
paper_title: ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant
    Tightness
paper_summary: 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/.
paper_ai_summary: 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.
paper_ai_keywords: 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