HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors

Panwang Pan, Zhuo Su, Chenguo Lin, Zhen Fan, Zhang Yongjie, Zeming Li, Tingting Shen, Yadong Mu, Yebin Liu

HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors: 12 upvotes on Hugging Face Daily Papers, #15 of 30 papers on 2024-06-19. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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