  The advancements in automatic text-to-3D generation have been remarkable.
Most existing methods use pre-trained text-to-image diffusion models to
optimize 3D representations like Neural Radiance Fields (NeRFs) via
latent-space denoising score matching. Yet, these methods often result in
artifacts and inconsistencies across different views due to their suboptimal
optimization approaches and limited understanding of 3D geometry. Moreover, the
inherent constraints of NeRFs in rendering crisp geometry and stable textures
usually lead to a two-stage optimization to attain high-resolution details.
This work proposes holistic sampling and smoothing approaches to achieve
high-quality text-to-3D generation, all in a single-stage optimization. We
compute denoising scores in the text-to-image diffusion model's latent and
image spaces. Instead of randomly sampling timesteps (also referred to as noise
levels in denoising score matching), we introduce a novel timestep annealing
approach that progressively reduces the sampled timestep throughout
optimization. To generate high-quality renderings in a single-stage
optimization, we propose regularization for the variance of z-coordinates along
NeRF rays. To address texture flickering issues in NeRFs, we introduce a kernel
smoothing technique that refines importance sampling weights coarse-to-fine,
ensuring accurate and thorough sampling in high-density regions. Extensive
experiments demonstrate the superiority of our method over previous approaches,
enabling the generation of highly detailed and view-consistent 3D assets
through a single-stage training process.
