FreeCloth: Free-form Generation Enhances Challenging Clothed Human Modeling

TL;DR

FreeCloth introduces a hybrid modeling framework that combines traditional deformation techniques with free-form generation to accurately animate challenging loose clothing on human avatars, significantly improving realism.

Contribution

The paper presents a novel hybrid approach that segments clothing regions and applies different modeling strategies, including a free-form generator for loose clothing, enhancing flexibility and detail.

Findings

Achieves state-of-the-art results on challenging clothing datasets.

Produces more realistic and detailed clothing deformations.

Outperforms existing methods in visual fidelity and realism.

Abstract

Achieving realistic animated human avatars requires accurate modeling of pose-dependent clothing deformations. Existing learning-based methods heavily rely on the Linear Blend Skinning (LBS) of minimally-clothed human models like SMPL to model deformation. However, they struggle to handle loose clothing, such as long dresses, where the canonicalization process becomes ill-defined when the clothing is far from the body, leading to disjointed and fragmented results. To overcome this limitation, we propose FreeCloth, a novel hybrid framework to model challenging clothed humans. Our core idea is to use dedicated strategies to model different regions, depending on whether they are close to or distant from the body. Specifically, we segment the human body into three categories: unclothed, deformed, and generated. We simply replicate unclothed regions that require no deformation. For deformed regions close to the body, we leverage LBS to handle the deformation. As for the generated regions, which correspond to loose clothing areas, we introduce a novel free-form, part-aware generator to model them, as they are less affected by movements. This free-form generation paradigm brings enhanced flexibility and expressiveness to our hybrid framework, enabling it to capture the intricate geometric details of challenging loose clothing, such as skirts and dresses. Experimental results on the benchmark dataset featuring loose clothing demonstrate that FreeCloth achieves state-of-the-art performance with superior visual fidelity and realism, particularly in the most challenging cases.