SlotMem: Character-Addressable Internal Memory for Narrative Long Video Generation
Researchers introduce SlotMem, a character-addressable internal memory framework designed for multi-character narrative long video generation. SlotMem employs a Character-Semantic Probe and Memory Encoder to compress visual tokens into role-specific slot memories, enabling more precise tracking of character identities. Experimental results on multiple benchmarks demonstrate that SlotMem improves long-range character consistency compared to existing methods, while maintaining similar video quality.
Why it matters: Maintaining consistent character identities across scene transitions is a major challenge in narrative video generation, and SlotMem offers a novel solution that advances this capability.
Full story at: arXiv Computer Vision ↗