Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable
  Single-stage Image-to-3D Generation
Existing feed-forward image-to-3D methods mainly rely on 2D multi-view
diffusion models that cannot guarantee 3D consistency. These methods easily
collapse when changing the prompt view direction and mainly handle
object-centric prompt images. In this paper, we propose a novel single-stage 3D
diffusion model, DiffusionGS, for object and scene generation from a single
view. DiffusionGS directly outputs 3D Gaussian point clouds at each timestep to
enforce view consistency and allow the model to generate robustly given prompt
views of any directions, beyond object-centric inputs. Plus, to improve the
capability and generalization ability of DiffusionGS, we scale up 3D training
data by developing a scene-object mixed training strategy. Experiments show
that our method enjoys better generation quality (2.20 dB higher in PSNR and
23.25 lower in FID) and over 5x faster speed (~6s on an A100 GPU) than SOTA
methods. The user study and text-to-3D applications also reveals the practical
values of our method. Our Project page at
https://caiyuanhao1998.github.io/project/DiffusionGS/ shows the video and
interactive generation results.
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Yuanhao Cai
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2024-11-22T16:28:35.610Z
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Yuanhao Cai
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CaiYuanhao
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He Zhang
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Kai Zhang
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Yixun Liang
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2024-11-22T16:29:02.377Z
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yixunliang
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lyxun
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Mengwei Ren
admin_assigned
2024-11-22T16:29:09.110Z
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Mengwei Ren
user
mengweir
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Fujun Luan
admin_assigned
2024-11-22T16:29:14.570Z
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Fujun Luan
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luanfujun
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Qing Liu
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Soo Ye Kim
claimed_verified
2025-09-24T13:59:09.133Z
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Soo Ye Kim
user
sooyek
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Jianming Zhang
admin_assigned
2024-11-22T16:29:28.371Z
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Jianming Zhang
user
jimmie33
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Zhifei Zhang
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Yuqian Zhou
6740a8d5bc8c37ec679fd374
Zhe Lin
6740a8d5bc8c37ec679fd375
Alan Yuille
Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable
  Single-stage Image-to-3D Generation
Existing feed-forward image-to-3D methods mainly rely on 2D multi-view
diffusion models that cannot guarantee 3D consistency. These methods easily
collapse when changing the prompt view direction and mainly handle
object-centric prompt images. In this paper, we propose a novel single-stage 3D
diffusion model, DiffusionGS, for object and scene generation from a single
view. DiffusionGS directly outputs 3D Gaussian point clouds at each timestep to
enforce view consistency and allow the model to generate robustly given prompt
views of any directions, beyond object-centric inputs. Plus, to improve the
capability and generalization ability of DiffusionGS, we scale up 3D training
data by developing a scene-object mixed training strategy. Experiments show
that our method enjoys better generation quality (2.20 dB higher in PSNR and
23.25 lower in FID) and over 5x faster speed (~6s on an A100 GPU) than SOTA
methods. The user study and text-to-3D applications also reveals the practical
values of our method. Our Project page at
https://caiyuanhao1998.github.io/project/DiffusionGS/ shows the video and
interactive generation results.
A novel single-stage 3D diffusion model, DiffusionGS, is proposed for generating 3D point clouds from a single view, offering better view consistency, quality, and speed compared to state-of-the-art methods.
feed-forward image-to-3D methods 2D multi-view diffusion models 3D diffusion models DiffusionGS 3D Gaussian point clouds view consistency scene-object mixed training strategy PSNR FID text-to-3D applications