Zero4D: Training-Free 4D Video Generation From Single Video Using Off-the-Shelf Video Diffusion Model

• URL: http://arxiv.org/abs/2503.22622v1
• Date: Fri, 28 Mar 2025 17:14:48 GMT
• Title: Zero4D: Training-Free 4D Video Generation From Single Video Using Off-the-Shelf Video Diffusion Model
• Authors: Jangho Park, Taesung Kwon, Jong Chul Ye,
• Abstract summary: 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.<n>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.
• Score: 52.0192865857058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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