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.

使用单个参考图像生成 3D 人体模型是具有挑战性的，本文提出了一种从单视角输入生成 3D 人体模型的框架 HumanRef，通过引入参考引导评分蒸馏采样（Ref-SDS）方法有效地将图像指导纳入生成过程，实现了生成具有精细几何结构、逼真纹理和视角一致性的 3D 服装人物。