LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval
  Score Matching

Yixun Liang; Xin Yang; Jiantao Lin; Haodong Li; Xiaogang Xu; Yingcong Chen

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Paper · arXiv 2311.11284

LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching

Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, Yingcong Chen

20 upvotesNovember 19, 2023arXiv 预印本

AI 摘要

Interval Score Matching improves text-to-3D generation by addressing Score Distillation Sampling's over-smoothing issue and using 3D Gaussian Splatting, achieving superior quality and efficiency.

Score Distillation SamplingInterval Score Matchingdeterministic diffusing trajectoriesinterval-based score matchingover-smoothing3D Gaussian Splattingtext-to-3D generation

Abstract

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.