Layered-Garment Net: Generating Multiple Implicit Garment Layers from a Single Image

TL;DR

This paper introduces Layered-Garment Net (LGN), a novel method for generating multiple intersection-free garment layers on human bodies from a single image, addressing a key challenge in realistic virtual human modeling.

Contribution

LGN is the first approach to generate multi-layer garments with guaranteed intersection-free geometry from a single image.

Findings

LGN effectively generates multiple garment layers without intersections.

It outperforms existing methods in multi-layer garment generation.

The approach ensures realistic layering consistent with human clothing.

Abstract

Recent research works have focused on generating human models and garments from their 2D images. However, state-of-the-art researches focus either on only a single layer of the garment on a human model or on generating multiple garment layers without any guarantee of the intersection-free geometric relationship between them. In reality, people wear multiple layers of garments in their daily life, where an inner layer of garment could be partially covered by an outer one. In this paper, we try to address this multi-layer modeling problem and propose the Layered-Garment Net (LGN) that is capable of generating intersection-free multiple layers of garments defined by implicit function fields over the body surface, given the person's near front-view image. With a special design of garment indication fields (GIF), we can enforce an implicit covering relationship between the signed distance fields (SDF) of different layers to avoid self-intersections among different garment surfaces and the human body. Experiments demonstrate the strength of our proposed LGN framework in generating multi-layer garments as compared to state-of-the-art methods. To the best of our knowledge, LGN is the first research work to generate intersection-free multiple layers of garments on the human body from a single image.