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

Abstract: 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.

• The paper introduces MOTIA, a two-phase framework that uses input-specific adaptation and pattern-aware outpainting to extend video boundaries.
• The methodology integrates spatial-aware insertion and LoRA adapters to enhance flexibility and scalability, outperforming state-of-the-art benchmarks like SSIM, LPIPS, and FVD.
• The approach produces visually coherent video outputs validated by user studies, paving the way for more flexible and robust video generative models.

Mastering Video Outpainting Through Input-Specific Adaptation: A Detailed Overview

"Be-Your-Outpainter" introduces a novel framework, MOTIA (Mastering Video Outpainting Through Input-Specific Adaptation), that addresses the challenges of video outpainting by leveraging intrinsic data-specific patterns. Video outpainting, which extends video content beyond existing boundaries while maintaining consistency, encounters issues in quality and flexibility with existing methods.

Core Contributions

MOTIA's foundation comprises two primary phases: input-specific adaptation and pattern-aware outpainting. The initial phase conducts pseudo-outpainting on the source video, allowing the model to identify significant patterns and bridge the generative and outpainting processes. The subsequent phase extends these patterns to achieve effective outpainting outcomes, enhanced by techniques such as spatial-aware insertion and noise travel.

Methodology

1. Input-Specific Adaptation: This phase focuses on training the model to recognize the source video's unique patterns. By applying random masks and augmentations, the model learns to denoise and reconstruct these regions, leveraging intrinsic video patterns. The incorporation of LoRA adapters ensures efficient tuning without excessive memory use.
2. Pattern-Aware Outpainting: Utilizing learned intrinsic patterns, this phase involves generating extended video content. Spatial-aware insertion dynamically adjusts pattern influence based on feature proximity, while noise regret mitigates conflicts during denoising, optimizing the generative process.

Technical Strengths

• Flexibility and Scalability: MOTIA is adaptable to various mask types and video formats, overcoming limitations prevalent in models dependent on extensive datasets and fixed resolutions.
• Integration with Pretrained Models: The architecture integrates a pre-existing text-to-image model (Stable Diffusion) with adaptations for video processing. ControlNet enhances the method's capacity to use masked conditions, enriching the overall outpainting process.

Results and Evaluation

MOTIA was extensively evaluated against state-of-the-art methods on benchmarks like DAVIS and YouTube-VOS. It demonstrated superior performance in SSIM, LPIPS, and FVD metrics, underscoring its effectiveness in generating visually coherent and perceptually realistic video outputs. User studies also favored MOTIA in terms of visual quality and realism, validating its practical applicability.

Discussion

The study highlights the importance of leveraging data-specific patterns within the source video, a concept less emphasized in prior approaches. By using input-specific adaptation to fine-tune generative models, this method delivers substantial improvements over traditional techniques, which often fail in out-domain scenarios. Additionally, the framework supports future extensions to long video processing, ensuring scalability without significant scalability issues.

Conclusion

The work represents significant advancement in video outpainting, suggesting promising avenues for further research. By focusing on intrinsic video characteristics and maintaining a robust adaptation mechanism, MOTIA paves the way for more flexible and universally applicable video generative models. The practical implications are noteworthy for applications requiring seamless video integration across diverse display environments and formats.