title: VideoScene: Distilling Video Diffusion Model to Generate 3D Scenes in
    One Step
summary: Recovering 3D scenes from sparse views is a challenging task due to its
  inherent ill-posed problem. Conventional methods have developed specialized
  solutions (e.g., geometry regularization or feed-forward deterministic model)
  to mitigate the issue. However, they still suffer from performance degradation
  by minimal overlap across input views with insufficient visual information.
  Fortunately, recent video generative models show promise in addressing this
  challenge as they are capable of generating video clips with plausible 3D
  structures. Powered by large pretrained video diffusion models, some pioneering
  research start to explore the potential of video generative prior and create 3D
  scenes from sparse views. Despite impressive improvements, they are limited by
  slow inference time and the lack of 3D constraint, leading to inefficiencies
  and reconstruction artifacts that do not align with real-world geometry
  structure. In this paper, we propose VideoScene to distill the video diffusion
  model to generate 3D scenes in one step, aiming to build an efficient and
  effective tool to bridge the gap from video to 3D. Specifically, we design a
  3D-aware leap flow distillation strategy to leap over time-consuming redundant
  information and train a dynamic denoising policy network to adaptively
  determine the optimal leap timestep during inference. Extensive experiments
  demonstrate that our VideoScene achieves faster and superior 3D scene
  generation results than previous video diffusion models, highlighting its
  potential as an efficient tool for future video to 3D applications. Project
  Page: https://hanyang-21.github.io/VideoScene
paper_authors._id: 67ee01265839c8a023344aee
paper_authors.name: Hanyang Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-03T07:52:06.284Z
paper_authors._id: 65c38f6c137aba2aee524989
paper_authors.avatarUrl: /avatars/a93e29f55876df3e65e2532972e057e4.svg
paper_authors.fullname: Hanyang Wang
paper_authors.type: user
paper_authors.user: hanyang-21
paper_authors._id: 67ee01265839c8a023344aef
paper_authors.name: Fangfu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T13:46:26.790Z
paper_authors._id: 6505a02f9310ce8c400edc63
paper_authors.avatarUrl: /avatars/bbf781594fc8c812316711aa8e2797aa.svg
paper_authors.fullname: Fangfu Liu
paper_authors.type: user
paper_authors.user: Liuff23
paper_authors._id: 67ee01265839c8a023344af0
paper_authors.name: Jiawei Chi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T13:46:47.647Z
paper_authors._id: 66e14cf2fb009ef598305fe5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/etohfcD9GKaRPir1HRP-5.jpeg
paper_authors.fullname: Jiawei Chi
paper_authors.type: user
paper_authors.user: isaac158
paper_authors._id: 67ee01265839c8a023344af1
paper_authors.name: Yueqi Duan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T13:46:55.497Z
paper_authors._id: 66c8131afafc0fc87ca99650
paper_authors.avatarUrl: /avatars/a6eeba2ccf011d5c9964fd38f85bd671.svg
paper_authors.fullname: Yueqi Duan
paper_authors.type: user
paper_authors.user: duanyueqi
paper_title: VideoScene: Distilling Video Diffusion Model to Generate 3D Scenes in
    One Step
paper_summary: Recovering 3D scenes from sparse views is a challenging task due to its
  inherent ill-posed problem. Conventional methods have developed specialized
  solutions (e.g., geometry regularization or feed-forward deterministic model)
  to mitigate the issue. However, they still suffer from performance degradation
  by minimal overlap across input views with insufficient visual information.
  Fortunately, recent video generative models show promise in addressing this
  challenge as they are capable of generating video clips with plausible 3D
  structures. Powered by large pretrained video diffusion models, some pioneering
  research start to explore the potential of video generative prior and create 3D
  scenes from sparse views. Despite impressive improvements, they are limited by
  slow inference time and the lack of 3D constraint, leading to inefficiencies
  and reconstruction artifacts that do not align with real-world geometry
  structure. In this paper, we propose VideoScene to distill the video diffusion
  model to generate 3D scenes in one step, aiming to build an efficient and
  effective tool to bridge the gap from video to 3D. Specifically, we design a
  3D-aware leap flow distillation strategy to leap over time-consuming redundant
  information and train a dynamic denoising policy network to adaptively
  determine the optimal leap timestep during inference. Extensive experiments
  demonstrate that our VideoScene achieves faster and superior 3D scene
  generation results than previous video diffusion models, highlighting its
  potential as an efficient tool for future video to 3D applications. Project
  Page: https://hanyang-21.github.io/VideoScene
paper_ai_summary: VideoScene distills video diffusion models for efficient one-step 3D scene generation from sparse views, enhancing speed and accuracy compared to previous methods.
paper_ai_keywords: video diffusion models 3D scenes sparse views leap flow distillation dynamic denoising policy network 3D-aware video generative models