BulletTime: Decoupled Control of Time and Camera Pose for Video Generation
Authors: name: Yiming Wang, name: Qihang Zhang, name: Shengqu Cai, name: Tong Wu, name: Jan Ackermann, name: Zhengfei Kuang, name: Yang Zheng, name: Frano Rajič, name: Siyu Tang, name: Gordon Wetzstein

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/