HD-Fusion: Detailed Text-to-3D Generation Leveraging Multiple Noise
  Estimation
Jinbo Wu Xiaobo Gao Xing Liu Zhengyang Shen Chen Zhao Haocheng Feng Jingtuo Liu Errui Ding
In this paper, we study Text-to-3D content generation leveraging 2D diffusion
priors to enhance the quality and detail of the generated 3D models. Recent
progress (Magic3D) in text-to-3D has shown that employing high-resolution
(e.g., 512 x 512) renderings can lead to the production of high-quality 3D
models using latent diffusion priors. To enable rendering at even higher
resolutions, which has the potential to further augment the quality and detail
of the models, we propose a novel approach that combines multiple noise
estimation processes with a pretrained 2D diffusion prior. Distinct from the
Bar-Tal et al.s' study which binds multiple denoised results to generate images
from texts, our approach integrates the computation of scoring distillation
losses such as SDS loss and VSD loss which are essential techniques for the 3D
content generation with 2D diffusion priors. We experimentally evaluated the
proposed approach. The results show that the proposed approach can generate
high-quality details compared to the baselines.