BulletTime: Decoupled Control of Time and Camera Pose for Video Generation

• URL: http://arxiv.org/abs/2512.05076v1
• Date: Thu, 04 Dec 2025 18:40:52 GMT
• Title: BulletTime: Decoupled Control of Time and Camera Pose for Video Generation
• Authors: Yiming Wang, Qihang Zhang, Shengqu Cai, Tong Wu, Jan Ackermann, Zhengfei Kuang, Yang Zheng, Frano Rajič, Siyu Tang, Gordon Wetzstein,
• Abstract summary: We introduce a 4D-controllable video diffusion framework that explicitly decouples scene dynamics from camera pose.<n>We show that our model achieves robust real-world 4D control across diverse timing patterns and camera trajectories.
• Score: 48.835425748367875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emerging video diffusion models achieve high visual fidelity but fundamentally couple scene dynamics with camera motion, limiting their ability to provide precise spatial and temporal control. We introduce a 4D-controllable video diffusion framework that explicitly decouples scene dynamics from camera pose, enabling fine-grained manipulation of both scene dynamics and camera viewpoint. Our framework takes continuous world-time sequences and camera trajectories as conditioning inputs, injecting them into the video diffusion model through a 4D positional encoding in the attention layer and adaptive normalizations for feature modulation. To train this model, we curate a unique dataset in which temporal and camera variations are independently parameterized; this dataset will be made public. Experiments show that our model achieves robust real-world 4D control across diverse timing patterns and camera trajectories, while preserving high generation quality and outperforming prior work in controllability. See our website for video results: https://19reborn.github.io/Bullet4D/

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