带纹理的语义人体网格重建
Semantic Human Mesh Reconstruction with Textures
AI总结：
本文提出SHERT流水线，通过语义与法线采样、自监督补全细化及图像文本驱动的纹理扩散，重建带稳定UV、高质量几何和一致语义的纹理人体网格。
摘要：
近年来，三维精细人体网格重建领域取得了显著进展。然而，由于结果不稳定、网格质量低以及缺乏UV展开和蒙皮权重，当前方法在工业应用中仍面临挑战。本文提出SHERT，这是一种能够重建带纹理且具有高精度细节的语义人体网格的新型流水线。SHERT在精细表面（如网格和SDF）与相应的SMPL-X模型之间应用基于语义和法线的采样，以获得部分采样的语义网格，随后通过我们专门设计的自监督补全与细化网络生成完整的语义网格。以完整语义网格为基础，我们采用纹理扩散模型创建由图像和文本共同驱动的人体纹理。我们重建的网格具有稳定的UV展开、高质量三角形网格和一致的语义信息。给定的SMPL-X模型提供语义信息和形状先验，使SHERT即使在输入不正确或不完整时也能表现良好。语义信息还使面部、身体和手部等不同身体部位的替换和动画制作变得容易。定量和定性实验表明，SHERT能够生成高保真且鲁棒的语义网格，其性能优于最先进的方法。
英文摘要：
The field of 3D detailed human mesh reconstruction has made significant progress in recent years. However, current methods still face challenges when used in industrial applications due to unstable results, low-quality meshes, and a lack of UV unwrapping and skinning weights. In this paper, we present SHERT, a novel pipeline that can reconstruct semantic human meshes with textures and high-precision details. SHERT applies semantic- and normal-based sampling between the detailed surface (e.g. mesh and SDF) and the corresponding SMPL-X model to obtain a partially sampled semantic mesh and then generates the complete semantic mesh by our specifically designed self-supervised completion and refinement networks. Using the complete semantic mesh as a basis, we employ a texture diffusion model to create human textures that are driven by both images and texts. Our reconstructed meshes have stable UV unwrapping, high-quality triangle meshes, and consistent semantic information. The given SMPL-X model provides semantic information and shape priors, allowing SHERT to perform well even with incorrect and incomplete inputs. The semantic information also makes it easy to substitute and animate different body parts such as the face, body, and hands. Quantitative and qualitative experiments demonstrate that SHERT is capable of producing high-fidelity and robust semantic meshes that outperform state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
