发表机构
Carnegie Mellon University; University of Washington; Texas A&M University; Google（卡内基梅隆大学; 华盛顿大学; 德克萨斯A&M大学; 谷歌）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对单图3D服装重建的纹理合成瓶颈，提出OmniFabric方法，在缝纫版型空间利用VLM先验和扩散Transformer实现全局相干纹理生成，显著优于现有基线。
AI 中文摘要
从单张图像自动生成可用于生产的3D服装资产是数字内容创作中的核心挑战。尽管近期生成模型在3D几何重建方面取得了显著进展，但高质量纹理的合成仍然是瓶颈。现有方法常将环境光照和阴影直接烘焙到纹理贴图中，或无法保持全局结构相干性，导致生成的资产无法用于物理模拟和重新照明。在本工作中，我们提出OmniFabric，一种新颖方法，直接在2D缝纫版型空间中合成全局相干的纹理贴图。给定单张参考图像，我们的流程利用估计的3D网格和强大的视觉-语言模型（VLM）的生成先验，在展开的缝纫版型上建立完整但粗略的纹理初始化。随后，我们利用一个专门的扩散Transformer，通过自动化合成数据引擎训练并以3D位置特征为条件，直接在规范UV域中细化该初始化。这有效去除了失真和烘焙伪影，提取出保留原始服装设计的干净且归一化的纹理贴图。大量实验表明，OmniFabric显著优于最先进的基线方法，生成具有高质量纹理的照片级逼真3D服装。
英文摘要
Automated generation of production-ready 3D garment assets from a single image is a central challenge in digital content creation. While recent generative models have significantly advanced 3D geometry reconstruction, synthesizing high-quality textures remains a bottleneck. Existing methods often bake environmental illumination and shadows directly into the texture map, or they fail to maintain global structural coherence, making the resulting assets unusable for physical simulation and relighting. In this work, we introduce OmniFabric, a novel approach that synthesizes globally coherent texture maps directly within the 2D sewing pattern space. Given a single reference image, our pipeline utilizes an estimated 3D mesh and generative priors of powerful Vision-Language Models (VLM) to establish a complete but coarse texture initialization across the unwrapped sewing patterns. We then leverage a specialized diffusion transformer, trained via an automated synthetic data engine and conditioned on 3D positional features, to refine this initialization directly in the canonical UV domain. This effectively removes distortion and baked-in artifacts to extract a clean and normalized texture map that preserves the original garment design. Extensive experiments demonstrate that OmniFabric significantly outperforms state-of-the-art baselines, yielding photorealistic 3D garments with high-quality textures.
CommentsAccepted to SIGGRAPH Asia 2026. Project Page: https://humansensinglab.github.io/OmniFabric/
