Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916980392460288 |
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| author | Chatterjee, Agneet Entezari, Rahim Zhuravinskyi, Maksym Lapin, Maksim Adithyan, Reshinth Raj, Amit Baral, Chitta Yang, Yezhou Jampani, Varun |
| author_facet | Chatterjee, Agneet Entezari, Rahim Zhuravinskyi, Maksym Lapin, Maksim Adithyan, Reshinth Raj, Amit Baral, Chitta Yang, Yezhou Jampani, Varun |
| contents | Recent advances in video generation have enabled high-fidelity video synthesis from user provided prompts. However, existing models and benchmarks fail to capture the complexity and requirements of professional video generation. Towards that goal, we introduce Stable Cinemetrics, a structured evaluation framework that formalizes filmmaking controls into four disentangled, hierarchical taxonomies: Setup, Event, Lighting, and Camera. Together, these taxonomies define 76 fine-grained control nodes grounded in industry practices. Using these taxonomies, we construct a benchmark of prompts aligned with professional use cases and develop an automated pipeline for prompt categorization and question generation, enabling independent evaluation of each control dimension. We conduct a large-scale human study spanning 10+ models and 20K videos, annotated by a pool of 80+ film professionals. Our analysis, both coarse and fine-grained reveal that even the strongest current models exhibit significant gaps, particularly in Events and Camera-related controls. To enable scalable evaluation, we train an automatic evaluator, a vision-language model aligned with expert annotations that outperforms existing zero-shot baselines. SCINE is the first approach to situate professional video generation within the landscape of video generative models, introducing taxonomies centered around cinematic controls and supporting them with structured evaluation pipelines and detailed analyses to guide future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation Chatterjee, Agneet Entezari, Rahim Zhuravinskyi, Maksym Lapin, Maksim Adithyan, Reshinth Raj, Amit Baral, Chitta Yang, Yezhou Jampani, Varun Computer Vision and Pattern Recognition Recent advances in video generation have enabled high-fidelity video synthesis from user provided prompts. However, existing models and benchmarks fail to capture the complexity and requirements of professional video generation. Towards that goal, we introduce Stable Cinemetrics, a structured evaluation framework that formalizes filmmaking controls into four disentangled, hierarchical taxonomies: Setup, Event, Lighting, and Camera. Together, these taxonomies define 76 fine-grained control nodes grounded in industry practices. Using these taxonomies, we construct a benchmark of prompts aligned with professional use cases and develop an automated pipeline for prompt categorization and question generation, enabling independent evaluation of each control dimension. We conduct a large-scale human study spanning 10+ models and 20K videos, annotated by a pool of 80+ film professionals. Our analysis, both coarse and fine-grained reveal that even the strongest current models exhibit significant gaps, particularly in Events and Camera-related controls. To enable scalable evaluation, we train an automatic evaluator, a vision-language model aligned with expert annotations that outperforms existing zero-shot baselines. SCINE is the first approach to situate professional video generation within the landscape of video generative models, introducing taxonomies centered around cinematic controls and supporting them with structured evaluation pipelines and detailed analyses to guide future research. |
| title | Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.26555 |