title: V3D: Video Diffusion Models are Effective 3D Generators
summary: 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
paper_authors._id: 65efc93ce9f53442ead9587d
paper_authors.name: Zilong Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-12T12:26:58.930Z
paper_authors._id: 644e3e5f030210812f413073
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/uW8TKV2sds97lFBwnt6JK.jpeg
paper_authors.fullname: Zilong Chen
paper_authors.type: user
paper_authors.user: heheyas
paper_authors._id: 65efc93ce9f53442ead9587e
paper_authors.name: Yikai Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-13T13:26:42.936Z
paper_authors._id: 64206b35e40f66bcd1e3c985
paper_authors.avatarUrl: /avatars/a8f830d1c47702f642045399e59c9fe8.svg
paper_authors.fullname: Yikai Wang
paper_authors.type: user
paper_authors.user: yikaiw
paper_authors._id: 65efc93ce9f53442ead9587f
paper_authors.name: Feng Wang
paper_authors._id: 65efc93ce9f53442ead95880
paper_authors.name: Zhengyi Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-12T12:28:31.270Z
paper_authors._id: 634e15aec1ce28f1de91c470
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634e15aec1ce28f1de91c470/MdtJKgE_fbHydhqESr-DH.png
paper_authors.fullname: Zhengyi Wang
paper_authors.type: user
paper_authors.user: Zhengyi
paper_authors._id: 65efc93ce9f53442ead95881
paper_authors.name: Huaping Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-12T12:28:40.798Z
paper_authors._id: 63cb64351b705cc951e98fc8
paper_authors.avatarUrl: /avatars/eea059d9295767d7695b5a4d5ff8f53c.svg
paper_authors.fullname: Liu
paper_authors.type: user
paper_authors.user: Huaping
paper_title: V3D: Video Diffusion Models are Effective 3D Generators
paper_summary: 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
paper_ai_keywords: video diffusion models V3D world simulation capacity 3D generation geometrical consistency prior multi-view consistent 3D generator state-of-the-art video diffusion model 360degree orbit frames self-quality meshes 3D Gaussians tailored reconstruction pipelines scene-level problem view selection camera path control sparse input performance quality multi-view consistency