SneakPeek: 基于未来的指导性视频生成
SneakPeek: Future-Guided Instructional Streaming Video Generation
AI总结：
SneakPeek通过预测因果适应、未来引导自我强制和多提示条件，实现了基于未来的指导性视频生成，提高了时间一致性和可控性。
摘要：
指导性视频生成是一项新兴任务，旨在从文本描述中合成连贯的程序性活动演示。这种能力在内容创作、教育和人机交互领域具有广泛的应用前景，但现有视频扩散模型在长序列的多个动作步骤中难以保持时间一致性和可控性。我们介绍了一种面向未来的指导性视频生成流水线，称为SneakPeek，是一种基于扩散的自回归框架，旨在根据初始图像和结构化文本提示生成精确的、分步骤的指导性视频。我们的方法引入了三个关键创新来增强一致性和可控性：（1）预测因果适应，其中因果模型学习进行下一帧预测并预测未来关键帧；（2）未来引导的自我强制，采用双区域KV缓存方案以解决推理时间的暴露偏差问题；（3）多提示条件，它提供了对多步骤指令的精细和程序性控制。这些组件共同缓解了时间漂移，保持了运动一致性，并实现了交互式视频生成，其中未来的提示更新动态影响正在进行的视频生成。实验结果表明，我们的方法生成了时间一致且语义忠实的指导性视频，能够准确遵循复杂的多步骤任务描述。
英文摘要：
Instructional video generation is an emerging task that aims to synthesize coherent demonstrations of procedural activities from textual descriptions. Such capability has broad implications for content creation, education, and human-AI interaction, yet existing video diffusion models struggle to maintain temporal consistency and controllability across long sequences of multiple action steps. We introduce a pipeline for future-driven streaming instructional video generation, dubbed SneakPeek, a diffusion-based autoregressive framework designed to generate precise, stepwise instructional videos conditioned on an initial image and structured textual prompts. Our approach introduces three key innovations to enhance consistency and controllability: (1) predictive causal adaptation, where a causal model learns to perform next-frame prediction and anticipate future keyframes; (2) future-guided self-forcing with a dual-region KV caching scheme to address the exposure bias issue at inference time; (3) multi-prompt conditioning, which provides fine-grained and procedural control over multi-step instructions. Together, these components mitigate temporal drift, preserve motion consistency, and enable interactive video generation where future prompt updates dynamically influence ongoing streaming video generation. Experimental results demonstrate that our method produces temporally coherent and semantically faithful instructional videos that accurately follow complex, multi-step task descriptions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
