Cinematic storytelling is profoundly shaped by the artful manipulation of photographic elements such as depth of field and exposure. These effects are crucial in conveying mood and creating aesthetic appeal. However, controlling these effects in generative video models remains highly challenging, as most existing methods are restricted to camera motion control. In this paper, we propose CineCtrl, the first video cinematic editing framework that provides fine control over professional camera parameters (e.g., bokeh, shutter speed). We introduce a decoupled cross-attention mechanism to disentangle camera motion from photographic inputs, allowing fine-grained, independent control without compromising scene consistency. To overcome the shortage of training data, we develop a comprehensive data generation strategy that leverages simulated photographic effects with a dedicated real-world collection pipeline, enabling the construction of a large-scale dataset for robust model training. Extensive experiments demonstrate that our model generates high-fidelity videos with precisely controlled, user-specified photographic camera effects.
@article{arxiv.2511.12921,
title = {Generative Photographic Control for Scene-Consistent Video Cinematic Editing},
author = {Huiqiang Sun and Liao Shen and Zhan Peng and Kun Wang and Size Wu and Yuhang Zang and Tianqi Liu and Zihao Huang and Xingyu Zeng and Zhiguo Cao and Wei Li and Chen Change Loy},
journal= {arXiv preprint arXiv:2511.12921},
year = {2025}
}