UniDream: Unifying Diffusion Priors for Relightable Text-to-3D
  Generation
Zexiang Liu Yangguang Li Youtian Lin Xin Yu Sida Peng Yan-Pei Cao Xiaojuan Qi Xiaoshui Huang Ding Liang Wanli Ouyang
Recent advancements in text-to-3D generation technology have significantly
advanced the conversion of textual descriptions into imaginative
well-geometrical and finely textured 3D objects. Despite these developments, a
prevalent limitation arises from the use of RGB data in diffusion or
reconstruction models, which often results in models with inherent lighting and
shadows effects that detract from their realism, thereby limiting their
usability in applications that demand accurate relighting capabilities. To
bridge this gap, we present UniDream, a text-to-3D generation framework by
incorporating unified diffusion priors. Our approach consists of three main
components: (1) a dual-phase training process to get albedo-normal aligned
multi-view diffusion and reconstruction models, (2) a progressive generation
procedure for geometry and albedo-textures based on Score Distillation Sample
(SDS) using the trained reconstruction and diffusion models, and (3) an
innovative application of SDS for finalizing PBR generation while keeping a
fixed albedo based on Stable Diffusion model. Extensive evaluations demonstrate
that UniDream surpasses existing methods in generating 3D objects with clearer
albedo textures, smoother surfaces, enhanced realism, and superior relighting
capabilities.