Video outpainting is a challenging task, aiming at generating video content outside the viewport of the input video while maintaining inter-frame and intra-frame consistency. Existing methods fall short in either generation quality or flexibility. We introduce MOTIA Mastering Video Outpainting Through Input-Specific Adaptation, a diffusion-based pipeline that leverages both the intrinsic data-specific patterns of the source video and the image/video generative prior for effective outpainting. MOTIA comprises two main phases: input-specific adaptation and pattern-aware outpainting. The input-specific adaptation phase involves conducting efficient and effective pseudo outpainting learning on the single-shot source video. This process encourages the model to identify and learn patterns within the source video, as well as bridging the gap between standard generative processes and outpainting. The subsequent phase, pattern-aware outpainting, is dedicated to the generalization of these learned patterns to generate outpainting outcomes. Additional strategies including spatial-aware insertion and noise travel are proposed to better leverage the diffusion model's generative prior and the acquired video patterns from source videos. Extensive evaluations underscore MOTIA's superiority, outperforming existing state-of-the-art methods in widely recognized benchmarks. Notably, these advancements are achieved without necessitating extensive, task-specific tuning.

翻译：摘要：视频外延绘制是一项具有挑战性的任务，旨在生成输入视频视口之外的视频内容，同时保持帧间与帧内一致性。现有方法在生成质量或灵活性方面存在不足。我们提出MOTIA（通过输入特定适配掌握视频外延绘制），一种基于扩散的流水线，它同时利用源视频固有的数据特定模式以及图像/视频生成先验知识以实现高效的外延绘制。MOTIA包含两个主要阶段：输入特定适配与模式感知外延绘制。输入特定适配阶段涉及对单次源视频进行高效且有效的伪外延绘制学习。该过程促使模型识别并学习源视频中的模式，同时弥合标准生成过程与外延绘制之间的差距。后续的模式感知外延绘制阶段专注于泛化这些已学习的模式以生成外延绘制结果。我们提出包括空间感知插入与噪声迁移在内的附加策略，以更好地利用扩散模型的生成先验以及从源视频中获取的视频模式。广泛的评估凸显了MOTIA的优越性，在广泛认可的基准测试中超越了现有最先进方法。值得注意的是，这些进展是在无需进行大量任务特定调优的情况下实现的。