title: Zero4D: Training-Free 4D Video Generation From Single Video Using
    Off-the-Shelf Video Diffusion Model
summary: Recently, multi-view or 4D video generation has emerged as a significant
  research topic. Nonetheless, recent approaches to 4D generation still struggle
  with fundamental limitations, as they primarily rely on harnessing multiple
  video diffusion models with additional training or compute-intensive training
  of a full 4D diffusion model with limited real-world 4D data and large
  computational costs. To address these challenges, here we propose the first
  training-free 4D video generation method that leverages the off-the-shelf video
  diffusion models to generate multi-view videos from a single input video. Our
  approach consists of two key steps: (1) By designating the edge frames in the
  spatio-temporal sampling grid as key frames, we first synthesize them using a
  video diffusion model, leveraging a depth-based warping technique for guidance.
  This approach ensures structural consistency across the generated frames,
  preserving spatial and temporal coherence. (2) We then interpolate the
  remaining frames using a video diffusion model, constructing a fully populated
  and temporally coherent sampling grid while preserving spatial and temporal
  consistency. Through this approach, we extend a single video into a multi-view
  video along novel camera trajectories while maintaining spatio-temporal
  consistency. Our method is training-free and fully utilizes an off-the-shelf
  video diffusion model, offering a practical and effective solution for
  multi-view video generation.
paper_authors._id: 67e9fd61b13aed34b44ace5b
paper_authors.name: Jangho Park
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-31T08:11:44.942Z
paper_authors._id: 67e9fc3797cd6860c81d5838
paper_authors.avatarUrl: /avatars/6c37731156bf52c123bd390823890d28.svg
paper_authors.fullname: Jangho Park
paper_authors.type: user
paper_authors.user: jhpark96
paper_authors._id: 67e9fd61b13aed34b44ace5c
paper_authors.name: Taesung Kwon
paper_authors._id: 67e9fd61b13aed34b44ace5d
paper_authors.name: Jong Chul Ye
paper_title: Zero4D: Training-Free 4D Video Generation From Single Video Using
    Off-the-Shelf Video Diffusion Model
paper_summary: Recently, multi-view or 4D video generation has emerged as a significant
  research topic. Nonetheless, recent approaches to 4D generation still struggle
  with fundamental limitations, as they primarily rely on harnessing multiple
  video diffusion models with additional training or compute-intensive training
  of a full 4D diffusion model with limited real-world 4D data and large
  computational costs. To address these challenges, here we propose the first
  training-free 4D video generation method that leverages the off-the-shelf video
  diffusion models to generate multi-view videos from a single input video. Our
  approach consists of two key steps: (1) By designating the edge frames in the
  spatio-temporal sampling grid as key frames, we first synthesize them using a
  video diffusion model, leveraging a depth-based warping technique for guidance.
  This approach ensures structural consistency across the generated frames,
  preserving spatial and temporal coherence. (2) We then interpolate the
  remaining frames using a video diffusion model, constructing a fully populated
  and temporally coherent sampling grid while preserving spatial and temporal
  consistency. Through this approach, we extend a single video into a multi-view
  video along novel camera trajectories while maintaining spatio-temporal
  consistency. Our method is training-free and fully utilizes an off-the-shelf
  video diffusion model, offering a practical and effective solution for
  multi-view video generation.
paper_ai_summary: A training-free method uses off-the-shelf video diffusion models and depth-based warping to generate multi-view videos from a single input video, maintaining spatio-temporal consistency.
paper_ai_keywords: video diffusion models multi-view video generation 4D video generation spatio-temporal sampling grid key frames depth-based warping spatial coherence temporal coherence