HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors
<|reference_start|>HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors: 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.<|reference_end|>
@article{pan2024humansplat:,
    title={HumanSplat: Generalizable Single-Image Human Gaussian Splatting with
  Structure Priors},
    author={Panwang Pan, Zhuo Su, Chenguo Lin, Zhen Fan, Yongjie Zhang, Zeming Li,
  Tingting Shen, Yadong Mu, Yebin Liu},
    journal={arXiv preprint arXiv:2406.12459},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12459},
    primaryClass={cs.CV}
}