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