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

• URL: http://arxiv.org/abs/2311.11284v3
• Date: Sat, 2 Dec 2023 02:57:54 GMT
• Title: LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching
• Authors: Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, Yingcong Chen
• Abstract summary: Recent advancements in text-to-3D generation have shown promise. Many methods base themselves on Score Distillation Sampling (SDS) We propose Interval Score Matching (ISM) to counteract over-smoothing.
• Score: 33.696757740830506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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