HumanRef: Single Image to 3D Human Generation via Reference-Guided
  Diffusion
Jingbo Zhang Xiaoyu Li Qi Zhang Yanpei Cao Ying Shan Jing Liao
Generating a 3D human model from a single reference image is challenging
because it requires inferring textures and geometries in invisible views while
maintaining consistency with the reference image. Previous methods utilizing 3D
generative models are limited by the availability of 3D training data.
Optimization-based methods that lift text-to-image diffusion models to 3D
generation often fail to preserve the texture details of the reference image,
resulting in inconsistent appearances in different views. In this paper, we
propose HumanRef, a 3D human generation framework from a single-view input. To
ensure the generated 3D model is photorealistic and consistent with the input
image, HumanRef introduces a novel method called reference-guided score
distillation sampling (Ref-SDS), which effectively incorporates image guidance
into the generation process. Furthermore, we introduce region-aware attention
to Ref-SDS, ensuring accurate correspondence between different body regions.
Experimental results demonstrate that HumanRef outperforms state-of-the-art
methods in generating 3D clothed humans with fine geometry, photorealistic
textures, and view-consistent appearances.