Despite recent advancements in high-fidelity human reconstruction techniques, the requirements for densely captured images or time-consuming per-instance optimization significantly hinder their applications in broader scenarios. To tackle these issues, we present HumanSplat which predicts the 3D Gaussian Splatting properties of any human from a single input image in a generalizable manner. In particular, HumanSplat comprises a 2D multi-view diffusion model and a latent reconstruction transformer with human structure priors that adeptly integrate geometric priors and semantic features within a unified framework. A hierarchical loss that incorporates human semantic information is further designed to achieve high-fidelity texture modeling and better constrain the estimated multiple views. Comprehensive experiments on standard benchmarks and in-the-wild images demonstrate that HumanSplat surpasses existing state-of-the-art methods in achieving photorealistic novel-view synthesis.
@article{arxiv.2406.12459,
title = {HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors},
author = {Panwang Pan and Zhuo Su and Chenguo Lin and Zhen Fan and Yongjie Zhang and Zeming Li and Tingting Shen and Yadong Mu and Yebin Liu},
journal= {arXiv preprint arXiv:2406.12459},
year = {2024}
}