Cycle3D: High-quality and Consistent Image-to-3D Generation via
  Generation-Reconstruction Cycle
Recent 3D large reconstruction models typically employ a two-stage process,
including first generate multi-view images by a multi-view diffusion model, and
then utilize a feed-forward model to reconstruct images to 3D content.However,
multi-view diffusion models often produce low-quality and inconsistent images,
adversely affecting the quality of the final 3D reconstruction. To address this
issue, we propose a unified 3D generation framework called Cycle3D, which
cyclically utilizes a 2D diffusion-based generation module and a feed-forward
3D reconstruction module during the multi-step diffusion process. Concretely,
2D diffusion model is applied for generating high-quality texture, and the
reconstruction model guarantees multi-view consistency.Moreover, 2D diffusion
model can further control the generated content and inject reference-view
information for unseen views, thereby enhancing the diversity and texture
consistency of 3D generation during the denoising process. Extensive
experiments demonstrate the superior ability of our method to create 3D content
with high-quality and consistency compared with state-of-the-art baselines.
66a85c530a24e5a2b885f6f3
Zhenyu Tang
admin_assigned
2024-07-30T09:03:27.641Z
63c09aa88d1175e3399e906e
/avatars/a1b8a89e83275f049f7bf75af9086d84.svg
Zhenyu Tang
user
royjames
66a85c530a24e5a2b885f6f4
Junwu Zhang
admin_assigned
2024-07-30T09:03:33.421Z
64979385e486365ca6b3c030
/avatars/589344a9db41873d78452560cca3d234.svg
Junwu Zhang
user
junwuzhang
66a85c530a24e5a2b885f6f5
Xinhua Cheng
admin_assigned
2024-07-30T09:03:38.711Z
645b4026f9d4ec91fdd516ce
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Xinhua Cheng
user
cxh0519
66a85c530a24e5a2b885f6f6
Wangbo Yu
admin_assigned
2024-07-30T09:03:46.173Z
63f095be6309c84d5f48848a
https://cdn-avatars.huggingface.co/v1/production/uploads/63f095be6309c84d5f48848a/pL2CKi-r-0mMfIGhYSAsm.jpeg
Wangbo Yu
user
Drexubery
66a85c530a24e5a2b885f6f7
Chaoran Feng
admin_assigned
2024-07-30T09:03:57.691Z
65250cda87ad4a39f84d482d
https://cdn-avatars.huggingface.co/v1/production/uploads/65250cda87ad4a39f84d482d/ryzc7hx6x09DbL4eiEBip.jpeg
Feng Chaoran
user
Falcary
66a85c530a24e5a2b885f6f8
Yatian Pang
66a85c530a24e5a2b885f6f9
Bin Lin
claimed_verified
2024-12-03T11:17:04.347Z
6367a8175bb06007ea099b8f
https://cdn-avatars.huggingface.co/v1/production/uploads/6367a8175bb06007ea099b8f/IjG7HyWyWRlVt_XwRbxRW.jpeg
linbin
user
LanguageBind
66a85c530a24e5a2b885f6fa
Li Yuan
claimed_verified
2025-04-04T07:11:43.526Z
66135a5e50350afe76beebce
/avatars/370a4b83949355feb050c2cb0425c264.svg
yl2488
user
yl2488
Cycle3D: High-quality and Consistent Image-to-3D Generation via
  Generation-Reconstruction Cycle
Recent 3D large reconstruction models typically employ a two-stage process,
including first generate multi-view images by a multi-view diffusion model, and
then utilize a feed-forward model to reconstruct images to 3D content.However,
multi-view diffusion models often produce low-quality and inconsistent images,
adversely affecting the quality of the final 3D reconstruction. To address this
issue, we propose a unified 3D generation framework called Cycle3D, which
cyclically utilizes a 2D diffusion-based generation module and a feed-forward
3D reconstruction module during the multi-step diffusion process. Concretely,
2D diffusion model is applied for generating high-quality texture, and the
reconstruction model guarantees multi-view consistency.Moreover, 2D diffusion
model can further control the generated content and inject reference-view
information for unseen views, thereby enhancing the diversity and texture
consistency of 3D generation during the denoising process. Extensive
experiments demonstrate the superior ability of our method to create 3D content
with high-quality and consistency compared with state-of-the-art baselines.
A Cycle3D framework uses a unified 2D diffusion and 3D reconstruction model to generate high-quality, consistent 3D content.
multi-view diffusion model feed-forward model 3D reconstruction 2D diffusion-based generation module feed-forward 3D reconstruction module multi-view consistency denoising process