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.

最近关于文本到3D生成的进展在生成模型中取得了重要的里程碑，为在各种现实场景下创建富有想象力的3D资产开辟了新的可能性。然而，尽管最近的文本到3D生成方法显示出一定的潜力，但它们在呈现详细和高质量的3D模型方面常常表现不佳。本文识别了Score Distillation Sampling方法中的一个显著缺陷，即它为3D模型提供了不一致且低质量的更新方向，从而导致过度平滑效果。为了解决这个问题，我们提出了一种新颖的方法，称为Interval Score Matching。ISM采用确定性扩散轨迹，并利用基于区间的分数匹配来抵消过度平滑。此外，我们将3D高斯喷洒技术引入我们的文本到3D生成流程。大量实验证明我们的模型在质量和训练效率方面大大优于现有方法。

LucidDreamer：基于区间评分匹配的高保真度文本到3D的生成方案