解决长视频生成中互动失效与误差累积问题，实现稳定分钟级视频生成。
BIFE: Better Interaction, Fewer Errors for Minute-Long Video Generation
- 采用语义稀疏缓存检索长程依赖，保持历史交互一致性。
- 引入块强制训练策略，使跨块生成更连贯，提升整体质量。
- 构建分钟级视频基准数据集，支持细粒度评估与漂移误差测量。
长视频生成是构建真实世界模型的关键步骤，需兼顾高视觉保真度与长时程交互一致性。近期自回归扩散模型通过重用键值缓存实现长时生成，但仍面临两大挑战：滑动窗口缓存导致长程交互丢失，以及误差累积随时间加剧、逐步降低生成质量。为此，我们提出BIFE框架，引入基于检索的语义稀疏键值缓存以实现长程条件建模，并设计块强制训练策略以增强跨块一致性。两者协同作用，在保持历史交互的同时抑制生成漂移，实现稳定且连贯的分钟级视频生成。我们还构建了InterVBench，一个带有精细块级标注的分钟级视频基准，并定义了视频漂移误差（Video Drift Error）指标。在InterVBench和VBench-Long上的大量实验表明，BIFE达到当前最优性能，相比基线在VDE-Subject上提升22.2%，在VDE-Clarity上提升19.4%。
原文摘要 · Abstract (English)
Long video generation is a critical step toward building realistic world models, requiring both high visual fidelity and long-range interaction consistency. Recent autoregressive diffusion models enable long-horizon generation through KV cache reuse, yet suffer from two fundamental challenges: failure to preserve long-range interactions due to sliding-window KV cache and error accumulation that progressively degrades generation quality over time. To address these issues, we propose BIFE, a framework that introduces a semantic sparse KV cache for retrieval-based long-range conditioning and a Block Forcing training strategy to enforce cross-block consistency. Together, these designs preserve historical interactions while mitigating drift, enabling stable and coherent minute-long video generation. We also introduce InterVBench, a minute-long video benchmark with fine-grained block-level annotations and Video Drift Error metrics. Extensive experiments on InterVBench and VBench-Long demonstrate that BIFE achieves state-of-the-art performance, including a 22.2% improvement on VDE-Subject and a 19.4% improvement on VDE-Clarity over baselines. Website: https://alibaba-damo-academy.github.io/BIFE. Code: https://github.com/alibaba-damo-academy/BIFE.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。