The paper introduces MOTIA (Mastering Video Outpainting Through Input-Specific Adaptation), a diffusion-based pipeline designed to generate video content outside the viewport of the input video while maintaining inter-frame and intra-frame consistency. MOTIA consists of two main phases: input-specific adaptation and pattern-aware outpainting. The input-specific adaptation phase involves pseudo outpainting learning on the source video to identify and learn intrinsic patterns, bridging the gap between standard generative processes and outpainting. The pattern-aware outpainting phase generalizes these learned patterns to generate outpainting outcomes. Additional strategies, such as spatial-aware insertion and noise regret, are proposed to leverage the diffusion model's generative prior and the acquired video patterns. Extensive evaluations demonstrate MOTIA's superiority over existing methods in widely recognized benchmarks, achieving significant improvements in metrics like PSNR, SSIM, LPIPS, and FVD. The method is flexible, handling various video formats and resolutions, and outperforms previous state-of-the-art methods in both quantitative metrics and user studies.The paper introduces MOTIA (Mastering Video Outpainting Through Input-Specific Adaptation), a diffusion-based pipeline designed to generate video content outside the viewport of the input video while maintaining inter-frame and intra-frame consistency. MOTIA consists of two main phases: input-specific adaptation and pattern-aware outpainting. The input-specific adaptation phase involves pseudo outpainting learning on the source video to identify and learn intrinsic patterns, bridging the gap between standard generative processes and outpainting. The pattern-aware outpainting phase generalizes these learned patterns to generate outpainting outcomes. Additional strategies, such as spatial-aware insertion and noise regret, are proposed to leverage the diffusion model's generative prior and the acquired video patterns. Extensive evaluations demonstrate MOTIA's superiority over existing methods in widely recognized benchmarks, achieving significant improvements in metrics like PSNR, SSIM, LPIPS, and FVD. The method is flexible, handling various video formats and resolutions, and outperforms previous state-of-the-art methods in both quantitative metrics and user studies.