Score Distillation Sampling (SDS) has exhibited remarkable performance in
conditional 3D content generation. However, a comprehensive understanding of
the SDS formulation is still lacking, hindering the development of 3D
generation. In this work, we present an interpretation of SDS as a combination
of three functional components: mode-disengaging, mode-seeking and
variance-reducing terms, and analyze the properties of each. We show that
problems such as over-smoothness and color-saturation result from the intrinsic
deficiency of the supervision terms and reveal that the variance-reducing term
introduced by SDS is sub-optimal. Additionally, we shed light on the adoption
of large Classifier-Free Guidance (CFG) scale for 3D generation. Based on the
analysis, we propose a simple yet effective approach named Stable Score
Distillation (SSD) which strategically orchestrates each term for high-quality
3D generation. Extensive experiments validate the efficacy of our approach,
demonstrating its ability to generate high-fidelity 3D content without
succumbing to issues such as over-smoothness and over-saturation, even under
low CFG conditions with the most challenging NeRF representation.

通过对 Score Distillation Sampling（SDS）进行解释，我们揭示了其作为三个功能组成部分 —— 模式分离、模式寻找和方差降低的组合，并分析了每个组成部分的特性。在此基础上，我们提出了一种名为稳定评分蒸馏（SSD）的简单而有效的方法，有效地协调每个组成部分，实现高质量的 3D 内容生成。大量实验证实了我们方法的有效性，即使在最具挑战性的 NeRF 表示下，也能生成高保真度的 3D 内容而不会出现过度平滑和过度饱和等问题。