论文标题
Fasthuman：在几分钟内重建高质量的穿衣人
FastHuman: Reconstructing High-Quality Clothed Human in Minutes
论文作者
论文摘要
我们提出了一种使用多视图的图像在几分钟内优化高质量服装的人体形状的方法。虽然传统的神经渲染方法仅使用渲染损失而难以将几何形状和外观删除，并且在计算密集程度上，我们的方法使用基于网格的斑块翘曲技术来确保多视图光度一致性和Sphere Harmonic（SH）照明有效地完善几何细节。我们采用了定向点云的形状表示和SH阴影，与隐式方法相比，这大大减少了优化和渲染时间。我们的方法在合成和现实世界数据集上表现出了令人鼓舞的结果，这使其成为快速生成高质量人体形状的有效解决方案。项目页面\ href {https://l1346792580123.github.io/nccsfs/} {https://l1346792580123.github.io/nccsfs/nccsfs/}
We propose an approach for optimizing high-quality clothed human body shapes in minutes, using multi-view posed images. While traditional neural rendering methods struggle to disentangle geometry and appearance using only rendering loss, and are computationally intensive, our method uses a mesh-based patch warping technique to ensure multi-view photometric consistency, and sphere harmonics (SH) illumination to refine geometric details efficiently. We employ oriented point clouds' shape representation and SH shading, which significantly reduces optimization and rendering times compared to implicit methods. Our approach has demonstrated promising results on both synthetic and real-world datasets, making it an effective solution for rapidly generating high-quality human body shapes. Project page \href{https://l1346792580123.github.io/nccsfs/}{https://l1346792580123.github.io/nccsfs/}
