  This paper presents Invariant Score Distillation (ISD), a novel method for
high-fidelity text-to-3D generation. ISD aims to tackle the over-saturation and
over-smoothing problems in Score Distillation Sampling (SDS). In this paper,
SDS is decoupled into a weighted sum of two components: the reconstruction term
and the classifier-free guidance term. We experimentally found that
over-saturation stems from the large classifier-free guidance scale and
over-smoothing comes from the reconstruction term. To overcome these problems,
ISD utilizes an invariant score term derived from DDIM sampling to replace the
reconstruction term in SDS. This operation allows the utilization of a medium
classifier-free guidance scale and mitigates the reconstruction-related errors,
thus preventing the over-smoothing and over-saturation of results. Extensive
experiments demonstrate that our method greatly enhances SDS and produces
realistic 3D objects through single-stage optimization.
