CineEvolve: An Agentic Self-Evolution Framework for Cinematic Multi-Shot Video Generation
In this paper, we propose CineEvolve, an agentic self-evolution framework for cinematic multi-shot video generation that unifies a Director and a Critic within a single vision-language policy.
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Key points
- Cinematic multi-shot video generation requires both narrative planning and iterative refinement.
- Emulating real-world filmmaking that couples iterative direction with critical feedback, we train CineEvolve via alternating reinforcement learning, where the Director surfaces flaws to sharpen the Critic, and the updated Critic guides the Director's targeted revisions.
- To stabilize co-evolution, we introduce Evolutionary GRPO (E-GRPO), leveraging historical anchoring and revision safeguarding to mitigate advantage inflation.
- Additionally, we contribute CineTraj, a 40K cinematic trajectory dataset spanning creation, critique, and revision.
Sources (1)
- [1]CineEvolve: An Agentic Self-Evolution Framework for Cinematic Multi-Shot Video GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 11:30 AM
In this paper, we propose CineEvolve, an agentic self-evolution framework for cinematic multi-shot video generation that unifies a Director and a Critic within a single vision-language policy.
Cinematic multi-shot video generation requires both narrative planning and iterative refinement.
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