HumanSplat: Generalizable Single-Image Human Gaussian Splatting with
  Structure Priors
Authors: name: Panwang Pan, name: Zhuo Su, name: Chenguo Lin, name: Zhen Fan, name: Yongjie Zhang, name: Zeming Li, name: Tingting Shen, name: Yadong Mu, name: Yebin Liu

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.