  Text-to-3D content creation is a rapidly evolving research area. Given the
scarcity of 3D data, current approaches often adapt pre-trained 2D diffusion
models for 3D synthesis. Among these approaches, Score Distillation Sampling
(SDS) has been widely adopted. However, the issue of over-smoothing poses a
significant limitation on the high-fidelity generation of 3D models. To address
this challenge, LucidDreamer replaces the Denoising Diffusion Probabilistic
Model (DDPM) in SDS with the Denoising Diffusion Implicit Model (DDIM) to
construct Interval Score Matching (ISM). However, ISM inevitably inherits
inconsistencies from DDIM, causing reconstruction errors during the DDIM
inversion process. This results in poor performance in the detailed generation
of 3D objects and loss of content. To alleviate these problems, we propose a
novel method named Exact Score Matching (ESM). Specifically, ESM leverages
auxiliary variables to mathematically guarantee exact recovery in the DDIM
reverse process. Furthermore, to effectively capture the dynamic changes of the
original and auxiliary variables, the LoRA of a pre-trained diffusion model
implements these exact paths. Extensive experiments demonstrate the
effectiveness of ESM in text-to-3D generation, particularly highlighting its
superiority in detailed generation.
