Generative Photographic Control for Scene-Consistent Video Cinematic Editing
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914160453877760 |
|---|---|
| author | Sun, Huiqiang Shen, Liao Peng, Zhan Wang, Kun Wu, Size Zang, Yuhang Liu, Tianqi Huang, Zihao Zeng, Xingyu Cao, Zhiguo Li, Wei Loy, Chen Change |
| author_facet | Sun, Huiqiang Shen, Liao Peng, Zhan Wang, Kun Wu, Size Zang, Yuhang Liu, Tianqi Huang, Zihao Zeng, Xingyu Cao, Zhiguo Li, Wei Loy, Chen Change |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12921 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Photographic Control for Scene-Consistent Video Cinematic Editing Sun, Huiqiang Shen, Liao Peng, Zhan Wang, Kun Wu, Size Zang, Yuhang Liu, Tianqi Huang, Zihao Zeng, Xingyu Cao, Zhiguo Li, Wei Loy, Chen Change Computer Vision and Pattern Recognition 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. |
| title | Generative Photographic Control for Scene-Consistent Video Cinematic Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.12921 |