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AI Research
arXiv CS.AI·1d ago·5 min read
REEL: Learning to Retrieve, Evaluate, and Evolve Visual Memory for Any Long-Horizon Multi-Shot Video Generation
TL;DR
REEL is a novel visual-memory planner designed for long-horizon video generation, enhancing cross-shot consistency by up to 20.7% through a structured memory policy. It integrates retrieval, evaluation, and evolution of visual memory across multiple shots.
✦ Why It Matters
Explore REEL to improve the consistency of your multi-shot video generation projects today.
Key Takeaways
- Implement REEL to enhance long-horizon video generation.
- Utilize the REEL-SFT-4k and REEL-RL-1k datasets for training.
- Adopt multi-shot GRPO for improved memory management.
- Test video outputs using ST-Bench and REEL-Bench for consistency.
How It Works
REEL operates by learning to retrieve relevant visual references, create missing visual elements, and update its memory based on previous shots. This is achieved through a unified tool interface that allows it to adapt to different video generators, ensuring that the same memory policy can be applied across various contexts.