HiFA: High-fidelity Text-to-3D with Advanced Diffusion Guidance
Authors: name: Joseph Zhu, name: Peiye Zhuang

Automatic text-to-3D synthesis has achieved remarkable advancements through
the optimization of 3D models. Existing methods commonly rely on pre-trained
text-to-image generative models, such as diffusion models, providing scores for
2D renderings of Neural Radiance Fields (NeRFs) and being utilized for
optimizing NeRFs. However, these methods often encounter artifacts and
inconsistencies across multiple views due to their limited understanding of 3D
geometry. To address these limitations, we propose a reformulation of the
optimization loss using the diffusion prior. Furthermore, we introduce a novel
training approach that unlocks the potential of the diffusion prior. To improve
3D geometry representation, we apply auxiliary depth supervision for
NeRF-rendered images and regularize the density field of NeRFs. Extensive
experiments demonstrate the superiority of our method over prior works,
resulting in advanced photo-realism and improved multi-view consistency.