Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional
  Video Generation
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.
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Agneet Chatterjee
claimed_verified
2025-10-01T10:21:13.467Z
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Agneet Chatterjee
user
agneet
68dcad4f4159d1f2418f9bc8
Rahim Entezari
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Maksym Zhuravinskyi
68dcad4f4159d1f2418f9bca
Maksim Lapin
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Reshinth Adithyan
68dcad4f4159d1f2418f9bcc
Amit Raj
68dcad4f4159d1f2418f9bcd
Chitta Baral
68dcad4f4159d1f2418f9bce
Yezhou Yang
68dcad4f4159d1f2418f9bcf
Varun Jampani
Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional
  Video Generation
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.
Stable Cinemetrics introduces a structured evaluation framework for professional video generation, using taxonomies to assess models across specific filmmaking controls.
video generation Stable Cinemetrics taxonomies Setup Event Lighting Camera fine-grained control nodes benchmark prompt categorization question generation human study automatic evaluator vision-language model zero-shot baselines SCINE