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We propose a unified framework aimed at enhancing the diffusion priors for 3Dgeneration tasks. Despite the critical importance of these tasks, existingmethodologies often struggle to generate high-caliber results. We begin byexamining the inherent limitations in previous diffusion priors. We identify adivergence between the diffusion priors and the training procedures ofdiffusion models that substantially impairs the quality of 3D generation. Toaddress this issue, we propose a novel, unified framework that iterativelyoptimizes both the 3D model and the diffusion prior. Leveraging the differentlearnable parameters of the diffusion prior, our approach offers multipleconfigurations, affording various trade-offs between performance andimplementation complexity. Notably, our experimental results demonstrate thatour method markedly surpasses existing techniques, establishing newstate-of-the-art in the realm of text-to-3D generation. Furthermore, ourapproach exhibits impressive performance on both NeRF and the newly introduced3D Gaussian Splatting backbones. Additionally, our framework yields insightfulcontributions to the understanding of recent score distillation methods, suchas the VSD and DDS loss.

Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting