Realistic reconstruction of 3D clothing from an image has wide applications, such as avatar creation and virtual try-on. This paper presents a novel framework that reconstructs the texture map for 3D garments from a single image with pose. Assuming that 3D garments are modeled by stitching 2D garment sewing patterns, our specific goal is to generate a texture image for the sewing patterns. A key component of our framework, the Texture Unwarper, infers the original texture image from the input clothing image, which exhibits warping and occlusion of texture due to the user's body shape and pose. The Texture Unwarper effectively transforms between the input and output images by mapping the latent spaces of the two images. By inferring the unwarped original texture of the input garment, our method helps reconstruct 3D garment models that can show high-quality texture images realistically deformed for new poses. We validate the effectiveness of our approach through a comparison with other methods and ablation studies. Additionally, we release a large dataset of garment sewing patterns with textures and images of avatars wearing the garments, which will be useful for future research on garment texture reconstruction and synthesis.

翻译：从单张图像实现三维服装的真实感重建在虚拟化身创建和虚拟试穿等领域具有广泛应用。本文提出一种新颖框架，可从包含姿态信息的单张图像重建三维服装的纹理贴图。基于三维服装由二维服装纸样拼接而成的假设，我们旨在为服装纸样生成纹理图像。该框架的核心组件——纹理解卷器（Texture Unwarper），可从输入服装图像推断出原始纹理图像，其中输入图像因人体体型和姿态存在纹理扭曲与遮挡。该解卷器通过映射两幅图像的潜在空间，有效实现输入与输出图像之间的转换。通过推断输入服装的未变形原始纹理，我们的方法有助于重建可针对新姿态逼真变形的高质量纹理三维服装模型。我们通过与现有方法的对比实验及消融研究验证了本方法的有效性。此外，我们发布了包含服装纸样纹理及穿戴虚拟化身图像的大型数据集，这将为服装纹理重建与合成领域的未来研究提供重要支持。