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In this paper, we study Text-to-3D content generation leveraging 2D diffusionpriors to enhance the quality and detail of the generated 3D models. Recentprogress (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 3Dmodels using latent diffusion priors. To enable rendering at even higherresolutions, which has the potential to further augment the quality and detailof the models, we propose a novel approach that combines multiple noiseestimation processes with a pretrained 2D diffusion prior. Distinct from theBar-Tal et al.s' study which binds multiple denoised results to generate imagesfrom texts, our approach integrates the computation of scoring distillationlosses such as SDS loss and VSD loss which are essential techniques for the 3Dcontent generation with 2D diffusion priors. We experimentally evaluated theproposed approach. The results show that the proposed approach can generatehigh-quality details compared to the baselines.

HD-Fusion: Detailed Text-to-3D Generation Leveraging Multiple Noise Estimation