让视频角色在长剧情中保持身份一致，通过可定位的角色内存实现。
SlotMem: Character-Addressable Internal Memory for Narrative Long Video Generation
- 用语义探针定位角色相关视觉特征，构建角色专属记忆槽。
- 角色记忆随生成过程保守更新，有效提升长时一致性。
- 适合需要多角色连续叙事的视频生成任务，如影视剧创作。
在叙事性长视频生成中，跨场景和长时间间隔下保持角色身份一致是核心挑战。现有方法多依赖与角色身份无关的全局线索检索记忆，而近期基于角色的方法仍使用粗粒度帧级键值记忆，将身份与偶然视觉因素混淆，且在有限记忆容量下缺乏持续更新机制。为此，我们提出SlotMem，一种面向多角色叙事长视频生成的角色可寻址内部记忆框架。具体地，SlotMem利用角色语义探针从交叉注意力响应中定位角色相关视觉标记，并通过记忆编码器将DiT标记压缩为紧凑的角色专属记忆槽。生成过程中，记忆写入器以保守方式更新每个角色的记忆，同时角色级交叉注意力仅将对应角色记忆注入到该角色的局部标记中。在多个叙事长视频生成基准上的实验表明，SlotMem在保持相近视频质量的前提下，显著提升了长时角色一致性。代码已开源。
原文摘要 · Abstract (English)
Maintaining recurring character identities across scene transitions and long temporal gaps is a central challenge in narrative long video generation. Methods targeting global consistency often retrieve memory using cues that are not aligned with character identity preservation, while recent character-centric variants still rely on coarse frame-level kv memory that entangles identity with incidental visual factors and lacks a continuous update mechanism under limited memory capacity. To address these limitations, we propose SlotMem, a character-addressable internal memory framework for multi-character narrative long video generation. Specifically, SlotMem uses a Character-Semantic Probe to localize character-relevant visual tokens from cross-attention responses, and a Memory Encoder to compress DiT tokens into compact role-wise slot memory. As generation proceeds, a Memory Writer conservatively updates each character's memory with new observations, while Character-Wise Cross-Attention retrieves the role memory and injects it only into localized tokens of the same character. Experiments on multiple narrative long video generation benchmarks show that SlotMem improves long-range character consistency over existing baselines, while maintaining comparable video quality. Our code is available at https://github.com/YilaiLiu-HKU/SlotMem.
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