Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific
  Adaptation
Fu-Yun Wang Xiaoshi Wu Zhaoyang Huang Xiaoyu Shi Dazhong Shen Guanglu Song Yu Liu Hongsheng Li
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.