Towards Unified Keyframe Propagation Models (2205.09731v1)

Abstract: Many video editing tasks such as rotoscoping or object removal require the propagation of context across frames. While transformers and other attention-based approaches that aggregate features globally have demonstrated great success at propagating object masks from keyframes to the whole video, they struggle to propagate high-frequency details such as textures faithfully. We hypothesize that this is due to an inherent bias of global attention towards low-frequency features. To overcome this limitation, we present a two-stream approach, where high-frequency features interact locally and low-frequency features interact globally. The global interaction stream remains robust in difficult situations such as large camera motions, where explicit alignment fails. The local interaction stream propagates high-frequency details through deformable feature aggregation and, informed by the global interaction stream, learns to detect and correct errors of the deformation field. We evaluate our two-stream approach for inpainting tasks, where experiments show that it improves both the propagation of features within a single frame as required for image inpainting, as well as their propagation from keyframes to target frames. Applied to video inpainting, our approach leads to 44% and 26% improvements in FID and LPIPS scores. Code at https://github.com/runwayml/guided-inpainting

• The paper proposes a two-stream approach that uses convolutions for high-frequency details and attention mechanisms for low-frequency context.
• It outperforms prior methods in image and video inpainting, achieving reductions of up to 44% in FID and 26% in LPIPS scores.
• The model advances automated video editing by effectively preserving textures and ensuring consistent context propagation across frames.

Overview of "Towards Unified Keyframe Propagation Models"

The paper "Towards Unified Keyframe Propagation Models" addresses significant shortcomings in current video editing tasks, particularly rotoscoping and object removal, by introducing an innovative dual-process system designed to refine the propagation of context across video frames. The authors highlight the insufficiencies of existing transformer-based models, particularly their predisposition towards low-frequency attention that impedes the transmission of high-frequency details such as textures. Their proposed two-stream approach, integrating both locally interacting features (LIF) using convolutions for high-frequency details and globally interacting features (GIF) leveraging attention for low-frequency modeling, aims to rectify these issues and enhance video inpainting efficacy.

Two-Stream Model Architecture

The core of the proposed method is a sophisticated two-stream model architecture:

• Locally Interacting Feature (LIF) Stream: This stream is responsible for preserving and propagating high-frequency components, which are critical for maintaining detail. It utilizes convolutional operations that behave as high-pass filters, thus counterbalancing the low-pass filtering effect inherent in attention mechanisms.
• Globally Interacting Feature (GIF) Stream: Dedicated to handling low-frequency information robustly. The attention mechanism is leveraged here to bolster the transmission of context across frames, accommodating challenges such as significant camera motions without necessitating precise frame alignments.

The dual-stream arrangement facilitates a comprehensive and nuanced model that integrates interactions both within frames and across the keyframes and target frames, capitalizing on the strengths of each mode of information processing.

Experimental Evaluation and Results

The paper documents a series of experiments that validate the efficacy of their model across diverse inpainting tasks:

• Single Image Inpainting: The proposed model outperforms alternatives like LaMa and standard transformers, demonstrating superior results in terms of Fréchet Inception Distance (FID), Learned Perceptual Image Patch Similarity (LPIPS), and Structural Similarity Index Measure (SSIM).
• Guided Image Inpainting: The proposed model with FFCs showcased significant enhancements in texture and detail propagation across frames compared to LaMa and a standalone transformer approach. This emphasizes the model's adeptness at synthesizing information across multiple frames to generate consistent high-quality inpaintings.
• Video Inpainting: The model excels in video inpainting tasks, notably reducing FID and LPIPS scores by 44% and 26%, respectively, demonstrating its capability to maintain fidelity and perceptual similarity in the reconstructions.

Implications and Future Directions

The paper presents key contributions to the field of video content creation, suggesting a potential shift toward more integrated video editing tools that require minimal manual effort. By addressing the limitations of global attention models and introducing a hybrid two-stream architecture, the research offers a compelling framework for future exploration in intelligent video editing, real-time content propagation, and even real-time video manipulation.

In terms of future developments, the authors suggest potential applications of their architecture in fields such as object segmentation and matting. The adaptability and scalability of the two-stream model indicate promising avenues for evolving a unified approach to tackle a broader range of video-processing tasks.

Conclusion

This paper contributes a substantial advancement in keyframe-based video editing by introducing a dual-mode approach that effectively combines local detail preservation with robust global context propagation. The methodology reflects a deliberate and insightful response to existing model deficits, advocating for an approach poised to significantly impact applications in automated video editing, with strong possibilities for further research and refinement in the domain of AI-enhanced multimedia processing.