3D Garments: Reconstructing Topologically Correct Geometry and High-Quality Texture from Two Garment Images
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Date
2025
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We present a fully integrated pipeline for generating topologically correct 3D meshes and high-fidelity textures of fashion garments. Our geometry reconstruction module takes two input images and employs a semi-signed distance field representation with shifted generalized winding numbers in a deep-learning framework to produce accurate, non-watertight meshes. To create realistic, high-resolution textures (up to 4K) that closely match the input, we combine diffusion-based inpainting with a differentiable renderer, further enhancing the quality through normal-guided projection to minimize projection distortions in the texture image. Our results demonstrate both precise geometry and richly detailed textures. In addition, we are making a portion of our high-quality training dataset publicly available, consisting of 250 lower-garment triangulated meshes with 4K textures.
Description
CCS Concepts: Computing methodologies → Computer Graphics, Artificial Intelligence
@inproceedings{10.2312:egs.20251047,
booktitle = {Eurographics 2025 - Short Papers},
editor = {Ceylan, Duygu and Li, Tzu-Mao},
title = {{3D Garments: Reconstructing Topologically Correct Geometry and High-Quality Texture from Two Garment Images}},
author = {Heße, Lisa and Yadav, Sunil},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-268-4},
DOI = {10.2312/egs.20251047}
}