用人体部件信息指导纹理生成，让衣服裤子不混在一起。
PARTE: Part-Guided Texturing for 3D Human Reconstruction from a Single Image
- 根据无纹理人体表面预测各部位标签，再引导纹理生成
- 在单图重建中实现更清晰的衣物纹理边界，避免颜色混杂
- 适合需要精细服装纹理的3D建模研究者
现有3D人体重建方法中，不同人体部位的纹理常出现错位。每部分如外套或裤子应保持独立纹理，不相互渗透。人体各部位的结构一致性是推断图像不可见区域纹理的关键线索。然而，多数方法未显式利用这种部位分割先验，导致纹理错位。为此，我们提出PARTE，以3D人体部位信息为关键引导，实现高质量3D纹理重建。框架包含两部分：首先，设计3D部位分割模块（PartSegmenter），从单图重建无纹理人体表面，并基于其预测人体部位标签；其次，引入部位引导纹理模块（PartTexturer），从预训练图像生成模型中获取人体部位纹理对齐的先验知识。大量实验表明，本方法在3D人体重建质量上达到当前最优水平。
原文摘要 · Abstract (English)
The misaligned human texture across different human parts is one of the main limitations of existing 3D human reconstruction methods. Each human part, such as a jacket or pants, should maintain a distinct texture without blending into others. The structural coherence of human parts serves as a crucial cue to infer human textures in the invisible regions of a single image. However, most existing 3D human reconstruction methods do not explicitly exploit such part segmentation priors, leading to misaligned textures in their reconstructions. In this regard, we present PARTE, which utilizes 3D human part information as a key guide to reconstruct 3D human textures. Our framework comprises two core components. First, to infer 3D human part information from a single image, we propose a 3D part segmentation module (PartSegmenter) that initially reconstructs a textureless human surface and predicts human part labels based on the textureless surface. Second, to incorporate part information into texture reconstruction, we introduce a part-guided texturing module (PartTexturer), which acquires prior knowledge from a pre-trained image generation network on texture alignment of human parts. Extensive experiments demonstrate that our framework achieves state-of-the-art quality in 3D human reconstruction. The project page is available at https://hygenie1228.github.io/PARTE/.
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