Heße, LisaYadav, SunilCeylan, DuyguLi, Tzu-Mao2025-05-092025-05-092025978-3-03868-268-41017-4656https://doi.org/10.2312/egs.20251047https://diglib.eg.org/handle/10.2312/egs20251047We 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Computer Graphics, Artificial IntelligenceComputing methodologies → Computer GraphicsArtificial Intelligence10.2312/egs.202510474 pages