Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D apparel, and cloth animation. It thus necessitates a simple and straightforward pipeline to obtain high-quality texture from simple input, such as 2D reference images. Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process. We propose a novel method, called Cloth2Tex, which eliminates the human burden in this process. Cloth2Tex is a self-supervised method that generates texture maps with reasonable layout and structural consistency. Another key feature of Cloth2Tex is that it can be used to support high-fidelity texture inpainting. This is done by combining Cloth2Tex with a prevailing latent diffusion model. We evaluate our approach both qualitatively and quantitatively and demonstrate that Cloth2Tex can generate high-quality texture maps and achieve the best visual effects in comparison to other methods. Project page: tomguluson92.github.io/projects/cloth2tex/

翻译：随着三维虚拟试穿、二维服装数字化至三维服饰、布料动画等应用对合成逼真着装人物的需求日益增长，三维服装的制造与设计变得极为重要。因此，亟需一种简单直接的管线，从简单输入（如二维参考图像）获取高质量纹理。传统基于翘曲的纹理生成方法需为每种服装手动选取大量控制点，过程耗时繁琐。我们提出名为Cloth2Tex的新方法，消除了这一过程中的人力负担。Cloth2Tex是一种自监督方法，可生成布局合理且结构一致的纹理贴图。其另一关键特性是支持高保真纹理修复，通过将Cloth2Tex与主流潜在扩散模型结合实现。我们对该方法进行了定性与定量评估，结果表明Cloth2Tex能生成高质量纹理贴图，且相较其他方法实现了最佳视觉效果。项目主页：tomguluson92.github.io/projects/cloth2tex/