Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D
  Prior
Fangfu Liu Diankun Wu Yi Wei Yongming Rao Yueqi Duan
Recently, 3D content creation from text prompts has demonstrated remarkable
progress by utilizing 2D and 3D diffusion models. While 3D diffusion models
ensure great multi-view consistency, their ability to generate high-quality and
diverse 3D assets is hindered by the limited 3D data. In contrast, 2D diffusion
models find a distillation approach that achieves excellent generalization and
rich details without any 3D data. However, 2D lifting methods suffer from
inherent view-agnostic ambiguity thereby leading to serious multi-face Janus
issues, where text prompts fail to provide sufficient guidance to learn
coherent 3D results. Instead of retraining a costly viewpoint-aware model, we
study how to fully exploit easily accessible coarse 3D knowledge to enhance the
prompts and guide 2D lifting optimization for refinement. In this paper, we
propose Sherpa3D, a new text-to-3D framework that achieves high-fidelity,
generalizability, and geometric consistency simultaneously. Specifically, we
design a pair of guiding strategies derived from the coarse 3D prior generated
by the 3D diffusion model: a structural guidance for geometric fidelity and a
semantic guidance for 3D coherence. Employing the two types of guidance, the 2D
diffusion model enriches the 3D content with diversified and high-quality
results. Extensive experiments show the superiority of our Sherpa3D over the
state-of-the-art text-to-3D methods in terms of quality and 3D consistency.