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; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 3202-3211

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 progresses in text-to-3D have 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 poten tial to further augment the quality and detail of the models, we propose a novel approach that combines multiple noise estimation processes with a pretrained diffusion prior. Distinct from the Bar-Tal et al.s' study which binds multiple denoised results [1] 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 on XXX. The results show that the proposed approach can generate high-quality details more than the baselines.