OmniFit: Multi-modal 3D Body Fitting via Scale-agnostic Dense Landmark Prediction

Zeyu Cai; Yuliang Xiu; Renke Wang; Zhijing Shao; Xiaoben Li; Siyuan Yu; Chao Xu; Yang Liu; Baigui Sun; Jian Yang; Zhenyu Zhang

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arXiv · 2604.21575

OmniFit: Multi-modal 3D Body Fitting via Scale-agnostic Dense Landmark Prediction

Zeyu Cai·Yuliang Xiu·Renke Wang·Zhijing Shao·Xiaoben Li·Siyuan Yu·Chao Xu·Yang Liu·Baigui Sun·Jian Yang·Zhenyu Zhang

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Abstract

Fitting an underlying body model to 3D clothed human assets has been extensively studied, yet most approaches focus on either single-modal inputs such as point clouds or multi-view images alone, often requiring a known metric scale. This constraint is frequently impractical, especially for AI-generated assets where scale distortion is common. We propose OmniFit, a method that can seamlessly handle diverse multi-modal inputs, including full scans, partial depth observations, and image captures, while remaining scale-agnostic for both real and synthetic assets. Our key innovation is a simple yet effective conditional transformer decoder that directly maps surface points to dense body landmarks, which are then used for SMPL-X parameter fitting. In addition, an optional plug-and-play image adapter incorporates visual cues to compensate for missing geometric information. We further introduce a dedicated scale predictor that rescales subjects to canonical body proportions. OmniFit substantially outperforms state-of-the-art methods by 57.1 to 80.9 percent across daily and loose clothing scenarios. To the best of our knowledge, it is the first body fitting method to surpass multi-view optimization baselines and the first to achieve millimeter-level accuracy on the CAPE and 4D-DRESS benchmarks.

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BibTeX RIS

Zeyu Cai, Yuliang Xiu, Renke Wang, Zhijing Shao, Xiaoben Li, Siyuan Yu, Chao Xu, Yang Liu, Baigui Sun, Jian Yang, Zhenyu Zhang. 2026-04-23. OmniFit: Multi-modal 3D Body Fitting via Scale-agnostic Dense Landmark Prediction. https://arxiv.org/abs/2604.21575

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