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.

翻译：近期文本到3D生成技术的进步标志着生成模型的一个重要里程碑，为跨多种现实场景创建富有想象力的3D资产开启了新可能。尽管现有方法展现出潜力，但其生成的3D模型在细节与高质量表现上仍存在不足，这一问题在基于分数蒸馏采样（SDS）的方法中尤为突出。本文揭示了SDS方法的显著缺陷：它为3D模型提供不一致且低质量的更新方向，导致过平滑现象。为解决该问题，我们提出一种名为区间分数匹配（ISM）的新方法。ISM采用确定性扩散轨迹，并利用基于区间的分数匹配来抑制过平滑效应。此外，我们将3D高斯溅射（3D Gaussian Splatting）集成到文本到3D生成流程中。大量实验表明，我们的模型在质量与训练效率上均大幅超越当前最优方法。