  The recent advancements in text-to-3D generation mark a significant milestone
in generative models, unlocking new possibilities for creating imaginative 3D
assets across various real-world scenarios. While recent advancements in
text-to-3D generation have shown promise, they often fall short in rendering
detailed and high-quality 3D models. This problem is especially prevalent as
many methods base themselves on Score Distillation Sampling (SDS). This paper
identifies a notable deficiency in SDS, that it brings inconsistent and
low-quality updating direction for the 3D model, causing the over-smoothing
effect. To address this, we propose a novel approach called Interval Score
Matching (ISM). ISM employs deterministic diffusing trajectories and utilizes
interval-based score matching to counteract over-smoothing. Furthermore, we
incorporate 3D Gaussian Splatting into our text-to-3D generation pipeline.
Extensive experiments show that our model largely outperforms the
state-of-the-art in quality and training efficiency.
