Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation
Agneet Chatterjee, Rahim Entezari, Maksym Zhuravinskyi, Maksim Lapin, Reshinth Adithyan, Amit Raj, Chitta Baral, &#x27;YZ&#x27; Yezhou Yang, Varun Jampani
Recent advances in text-to-video (T2V) generation have enabled high-fidelity video synthesis from natural language prompts. However, existing models and benchmarks fail to capture the complexity and requirements of professional video generation. Towards that goal, we introduce Stable Cinemetrics (SCINE), a structured evaluation framework that formalizes filmmaking principles 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 formalize professional video generation within the landscape of video generative models, introducing taxonomies centered around cinematic control and supporting them with structured evaluation pipelines and detailed analyses to guide future research.