Be-Your-Outpainter：通过输入特定适应实现视频修复技术的掌握

Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation

March 20, 2024

作者: Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang, Xiaoyu Shi, Dazhong Shen, Guanglu Song, Yu Liu, Hongsheng Li

cs.AI

摘要

视频外部修复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，同时保持帧间和帧内一致性。现有方法在生成质量或灵活性方面存在不足。我们引入了通过输入特定适应性精通视频外部修复的MOTIA（Mastering Video Outpainting Through Input-Specific Adaptation）方法，这是一个基于扩散的流程，利用源视频的固有数据特定模式和图像/视频生成先验进行有效的外部修复。MOTIA包括两个主要阶段：输入特定适应和模式感知外部修复。输入特定适应阶段涉及对单镜头源视频进行高效有效的伪外部修复学习。这个过程鼓励模型识别和学习源视频中的模式，同时弥合标准生成过程与外部修复之间的差距。随后的模式感知外部修复阶段致力于将这些学习到的模式推广，生成外部修复结果。提出了包括空间感知插入和噪声传播在内的额外策略，以更好地利用扩散模型的生成先验和从源视频中获得的视频模式。广泛的评估突显了MOTIA的优越性，在广泛认可的基准测试中胜过现有的最先进方法。值得注意的是，这些进展是在不需要进行广泛的、特定任务的调整的情况下实现的。

English

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.