FastHuman: Reconstructing High-Quality Clothed Human in Minutes
This provides a faster solution for generating human body shapes, which is incremental over existing neural rendering methods.
The paper tackles the problem of reconstructing high-quality clothed human body shapes from multi-view posed images, achieving results in minutes by using a mesh-based patch warping technique and sphere harmonics illumination to reduce optimization and rendering times compared to implicit methods.
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/}