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arXiv · 2211.14485

FastHuman: Reconstructing High-Quality Clothed Human in Minutes

Lixiang Lin·Songyou Peng·Qijun Gan·Jianke Zhu
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Abstract

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/}

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Lixiang Lin, Songyou Peng, Qijun Gan, Jianke Zhu. 2022-11-26. FastHuman: Reconstructing High-Quality Clothed Human in Minutes. https://arxiv.org/abs/2211.14485

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