title: LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval
    Score Matching
summary: 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.
paper_authors._id: 655c3718b9d6d283fbf30de0
paper_authors.name: Yixun Liang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-21T10:24:16.025Z
paper_authors._id: 647d8f65becb41a272907e7a
paper_authors.avatarUrl: /avatars/f891a1986a9361b23b7c83c23031cf26.svg
paper_authors.fullname: yixunliang
paper_authors.type: user
paper_authors.user: lyxun
paper_authors._id: 655c3718b9d6d283fbf30de1
paper_authors.name: Xin Yang
paper_authors._id: 655c3718b9d6d283fbf30de2
paper_authors.name: Jiantao Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-21T13:53:53.795Z
paper_authors._id: 6332e2689bf698ce68a22e8c
paper_authors.avatarUrl: /avatars/c1922acfda2e6d2fe7b03194a404eb10.svg
paper_authors.fullname: JIANTAO LIN
paper_authors.type: user
paper_authors.user: LTT
paper_authors._id: 655c3718b9d6d283fbf30de3
paper_authors.name: Haodong Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-21T13:54:14.581Z
paper_authors._id: 641d211e353524fe41f16387
paper_authors.avatarUrl: /avatars/c6e72c82c029b415a035beebee50b52c.svg
paper_authors.fullname: Haodong Li
paper_authors.type: user
paper_authors.user: haodongli
paper_authors._id: 655c3718b9d6d283fbf30de4
paper_authors.name: Xiaogang Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-21T13:54:21.060Z
paper_authors._id: 637ee45b2438d7485b8d8f6a
paper_authors.avatarUrl: /avatars/11b7d29b6fa6c1b392641e0cd4002863.svg
paper_authors.fullname: Xiaogang Xu
paper_authors.type: user
paper_authors.user: xiaogang00
paper_authors._id: 655c3718b9d6d283fbf30de5
paper_authors.name: Yingcong Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-21T14:44:31.772Z
paper_authors._id: 655cba1d87b67834000590e8
paper_authors.avatarUrl: /avatars/3bd43b7c9351f65b8f38f4c8237a0146.svg
paper_authors.fullname: Yingcong Chen
paper_authors.type: user
paper_authors.user: yingcongchen
paper_title: LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval
    Score Matching
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Score Distillation Sampling Interval Score Matching deterministic diffusing trajectories interval-based score matching over-smoothing 3D Gaussian Splatting text-to-3D generation