V3D: Video Diffusion Models are Effective 3D Generators
Automatic 3D generation has recently attracted widespread attention. Recent
methods have greatly accelerated the generation speed, but usually produce
less-detailed objects due to limited model capacity or 3D data. Motivated by
recent advancements in video diffusion models, we introduce V3D, which
leverages the world simulation capacity of pre-trained video diffusion models
to facilitate 3D generation. To fully unleash the potential of video diffusion
to perceive the 3D world, we further introduce geometrical consistency prior
and extend the video diffusion model to a multi-view consistent 3D generator.
Benefiting from this, the state-of-the-art video diffusion model could be
fine-tuned to generate 360degree orbit frames surrounding an object given a
single image. With our tailored reconstruction pipelines, we can generate
high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method
can be extended to scene-level novel view synthesis, achieving precise control
over the camera path with sparse input views. Extensive experiments demonstrate
the superior performance of the proposed approach, especially in terms of
generation quality and multi-view consistency. Our code is available at
https://github.com/heheyas/V3D
65efc93ce9f53442ead9587d
Zilong Chen
admin_assigned
2024-03-12T12:26:58.930Z
644e3e5f030210812f413073
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/uW8TKV2sds97lFBwnt6JK.jpeg
Zilong Chen
user
heheyas
65efc93ce9f53442ead9587e
Yikai Wang
claimed_verified
2024-03-13T13:26:42.936Z
64206b35e40f66bcd1e3c985
/avatars/a8f830d1c47702f642045399e59c9fe8.svg
Yikai Wang
user
yikaiw
65efc93ce9f53442ead9587f
Feng Wang
65efc93ce9f53442ead95880
Zhengyi Wang
admin_assigned
2024-03-12T12:28:31.270Z
634e15aec1ce28f1de91c470
https://cdn-avatars.huggingface.co/v1/production/uploads/634e15aec1ce28f1de91c470/MdtJKgE_fbHydhqESr-DH.png
Zhengyi Wang
user
Zhengyi
65efc93ce9f53442ead95881
Huaping Liu
admin_assigned
2024-03-12T12:28:40.798Z
63cb64351b705cc951e98fc8
/avatars/eea059d9295767d7695b5a4d5ff8f53c.svg
Liu
user
Huaping
V3D: Video Diffusion Models are Effective 3D Generators
Automatic 3D generation has recently attracted widespread attention. Recent
methods have greatly accelerated the generation speed, but usually produce
less-detailed objects due to limited model capacity or 3D data. Motivated by
recent advancements in video diffusion models, we introduce V3D, which
leverages the world simulation capacity of pre-trained video diffusion models
to facilitate 3D generation. To fully unleash the potential of video diffusion
to perceive the 3D world, we further introduce geometrical consistency prior
and extend the video diffusion model to a multi-view consistent 3D generator.
Benefiting from this, the state-of-the-art video diffusion model could be
fine-tuned to generate 360degree orbit frames surrounding an object given a
single image. With our tailored reconstruction pipelines, we can generate
high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method
can be extended to scene-level novel view synthesis, achieving precise control
over the camera path with sparse input views. Extensive experiments demonstrate
the superior performance of the proposed approach, especially in terms of
generation quality and multi-view consistency. Our code is available at
https://github.com/heheyas/V3D
V3D leverages video diffusion models to generate high-quality, multi-view consistent 3D objects and scenes from single images with rapid reconstruction times.
video diffusion models geometrical consistency prior multi-view consistent 3D generator 360degree orbit frames high-quality meshes 3D Gaussians scene-level novel view synthesis camera path control sparse input views