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.

HumanSplat 通过在单个输入图像中预测任何人类的三维高斯 Splatting 属性，以解决高度精确的人体重建技术在广泛场景中受限于图像密集捕获要求或耗时的单例优化的问题。它由 2D 多视角扩散模型和具有人体结构先验的潜在重建转换器组成，巧妙地将几何先验和语义特征融入统一框架内。进一步设计了一种包含人体语义信息的分层损失，以实现高度逼真的纹理建模并更好地约束估计的多个视角。在标准基准和野外图像上的综合实验证明 HumanSplat 在实现逼真的新视图合成方面超越了现有最先进的方法。