  Although Score Distillation Sampling (SDS) has exhibited remarkable
performance in conditional 3D content generation, a comprehensive understanding
of its formulation is still lacking, hindering the development of 3D
generation. In this work, we decompose SDS as a combination of three functional
components, namely mode-seeking, mode-disengaging and variance-reducing terms,
analyzing the properties of each. We show that problems such as over-smoothness
and implausibility result from the intrinsic deficiency of the first two terms
and propose a more advanced variance-reducing term than that introduced by SDS.
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 and can be readily incorporated to various 3D
generation frameworks and 3D representations. 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.
