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The advancements in automatic text-to-3D generation have been remarkable.Most existing methods use pre-trained text-to-image diffusion models tooptimize 3D representations like Neural Radiance Fields (NeRFs) vialatent-space denoising score matching. Yet, these methods often result inartifacts and inconsistencies across different views due to their suboptimaloptimization approaches and limited understanding of 3D geometry. Moreover, theinherent constraints of NeRFs in rendering crisp geometry and stable texturesusually lead to a two-stage optimization to attain high-resolution details.This work proposes holistic sampling and smoothing approaches to achievehigh-quality text-to-3D generation, all in a single-stage optimization. Wecompute denoising scores in the text-to-image diffusion model's latent andimage spaces. Instead of randomly sampling timesteps (also referred to as noiselevels in denoising score matching), we introduce a novel timestep annealingapproach that progressively reduces the sampled timestep throughoutoptimization. To generate high-quality renderings in a single-stageoptimization, we propose regularization for the variance of z-coordinates alongNeRF rays. To address texture flickering issues in NeRFs, we introduce a kernelsmoothing technique that refines importance sampling weights coarse-to-fine,ensuring accurate and thorough sampling in high-density regions. Extensiveexperiments demonstrate the superiority of our method over previous approaches,enabling the generation of highly detailed and view-consistent 3D assetsthrough a single-stage training process.

HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance