CloSET: Modeling Clothed Humans on Continuous Surface with Explicit Template Decomposition

Creating animatable avatars from static scans requires the modeling of clothing deformations in different poses. Existing learning-based methods typically add pose-dependent deformations upon a minimally-clothed mesh template or a learned implicit template, which have limitations in capturing details or hinder end-to-end learning. In this paper, we revisit point-based solutions and propose to decompose explicit garment-related templates and then add pose-dependent wrinkles to them. In this way, the clothing deformations are disentangled such that the pose-dependent wrinkles can be better learned and applied to unseen poses. Additionally, to tackle the seam artifact issues in recent state-of-the-art point-based methods, we propose to learn point features on a body surface, which establishes a continuous and compact feature space to capture the fine-grained and pose-dependent clothing geometry. To facilitate the research in this field, we also introduce a high-quality scan dataset of humans in real-world clothing. Our approach is validated on two existing datasets and our newly introduced dataset, showing better clothing deformation results in unseen poses. The project page with code and dataset can be found at https://www.liuyebin.com/closet.

1. Introduction

Animating 3D clothed humans requires the modeling of pose-dependent deformations in various poses. The diversity of clothing styles and body poses makes this task extremely challenging. Traditional methods are based on either simple rigging and skinning [4], [20], [33] or physics-based simulation [14], [23], [24], [49], which heavily rely on artist efforts or computational resources. Recent learning-based methods [10], [36], [39], [59] resort to modeling the clothing deformation directly from raw scans of clothed humans. Despite the promising progress, this task is still far from being solved due to the challenges in clothing representations, generalization to unseen poses, and data acquisition, etc. Figure 1.

Our method learns to decompose garment templates (top row) and add pose-dependent wrinkles upon them (bottom row).