# Memento：通过主体重建实现长视频一致性生成

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-12 08:00
- AIHOT 分数：43
- AIHOT 链接：https://aihot.virxact.com/items/cmqgdr5tw0025slms4pq13fpz
- 原文链接：https://arxiv.org/abs/2606.14667

## AI 摘要

长视频生成中，主体在不同镜头、视角和场景切换下容易丢失。Memento 将主体保持视为身份锚定问题，联合训练自回归下一镜头生成与基于记忆的主体重建，利用历史记忆和全局描述恢复外观；双查询记忆机制分别检索长程身份记忆和短上下文关键帧。主体感知的数据流水线通过无代词描述提供重建监督。实验表明 Memento 在长期主体一致性、跨镜头连贯性和视觉质量上达到 SOTA。

## 正文

Long-form video generation requires recurring subjects to remain consistent across various shots, viewpoints, motions, and scene transitions. Existing temporal decomposition methods improve scalability by generating videos shot by shot. However, they mainly focus on optimizing plausible next-shot continuations without verifying whether the historical memory preserves identity-critical subject evidence. Consequently, as generation proceeds, recurring subjects may be diluted, overwritten, or forgotten. In this paper, we propose Memento, a subject-reconstruction-guided framework that treats subject preservation as an explicit identity grounding problem, based on the premise that a memory bank faithfully preserving a subject should support reconstructing that subject from memory alone. Specifically, Memento jointly trains autoregressive next-shot generation with memory-based subject reconstruction, recovering target appearances using historical memory and global story captions. To disentangle long-range subject evidence from short-range cues, Memento introduces a dual-query memory mechanism, where one query retrieves identity-relevant memory and the other selects short-context keyframes for coherent continuation. Additionally, a subject-aware cinematic data pipeline provides precise reconstruction supervision via consistent, pronoun-free subject descriptions. Experiments demonstrate that Memento achieves state-of-the-art performance in long-term subject consistency, cross-shot coherence, and visual quality.