ETCH：通过等变紧致性将身体拟合推广至着装人体

摘要

将人体适配到三维着装人体点云是一项常见却极具挑战性的任务。传统的基于优化的方法采用多阶段流程，对姿态初始化敏感；而近期基于学习的方法则常在处理多样化姿态和服装类型时面临泛化难题。我们提出了面向着装人体的等变紧密度拟合方法，简称ETCH，这是一种新颖的流程，通过局部近似SE(3)等变性来估计衣物到体表的映射，将紧密度编码为从衣物表面到内在身体的位移向量。基于此映射，姿态不变的身体特征回归稀疏的身体标记点，从而将着装人体拟合简化为内部身体标记点拟合任务。在CAPE和4D-Dress数据集上的大量实验表明，ETCH在宽松衣物下的身体拟合精度（提升16.7%至69.5%）和形状精度（平均提升49.9%）上显著超越了现有最先进方法——无论是忽略紧密度还是考虑紧密度的方案。我们的等变紧密度设计甚至能在一次性（或分布外）设置中将方向误差减少67.2%至89.8%。定性结果展示了ETCH在面对挑战性姿态、未见过的体型、宽松衣物及非刚性动态时的强大泛化能力。我们即将在https://boqian-li.github.io/ETCH/发布代码和模型，以供研究之用。

English

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：通过等变紧致性将身体拟合推广至着装人体

ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness

摘要

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