Towards An End-to-End Framework for Flow-Guided Video Inpainting (2204.02663v2)

Abstract: Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories. However, the hand-crafted flow-based processes in these methods are applied separately to form the whole inpainting pipeline. Thus, these methods are less efficient and rely heavily on the intermediate results from earlier stages. In this paper, we propose an End-to-End framework for Flow-Guided Video Inpainting (E$^2$FGVI) through elaborately designed three trainable modules, namely, flow completion, feature propagation, and content hallucination modules. The three modules correspond with the three stages of previous flow-based methods but can be jointly optimized, leading to a more efficient and effective inpainting process. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively and shows promising efficiency. The code is available at https://github.com/MCG-NKU/E2FGVI.

• The paper presents an end-to-end framework combining flow completion, feature propagation, and content hallucination to optimize video inpainting.
• It employs deformable convolutions and a temporal focal transformer to achieve accurate, coherent, and efficient inpainting results.
• Experimental results on YouTube-VOS and DAVIS demonstrate significant improvements in PSNR, SSIM, and processing speed compared to state-of-the-art methods.

An Overview of "Towards An End-to-End Framework for Flow-Guided Video Inpainting"

The paper "Towards An End-to-End Framework for Flow-Guided Video Inpainting" introduces an innovative approach to video inpainting using a novel end-to-end framework, labeled E$^2$FGVI. This framework synergistically combines three meticulously designed modules: flow completion, feature propagation, and content hallucination, which jointly optimize the inpainting process in contrast to traditional multi-stage, hand-crafted methods.

Framework Components

1. Flow Completion Module: This component capitalizes on a lightweight optical flow model to fill in missing motion information in video frames. It encourages task-oriented flow completion through end-to-end training, enhancing both efficiency and accuracy over prior isolated methods.
2. Feature Propagation Module: Utilizing deformable convolution, this module tackles pixel propagation in a feature space rather than on pixel data directly. This approach not only speeds up computations via GPU but also significantly mitigates issues arising from inaccurate flow estimations. The module achieves this by adaptively merging valid information across temporal neighbors.
3. Content Hallucination Module: Integrating a temporal focal transformer enables this module to efficiently manage long-range temporal dependencies, ensuring coherence across frames. By considering both local and global features, it offers robust spatial and temporal consistency in inpainting results.

Experimental Results

The authors conducted extensive experiments on YouTube-VOS and DAVIS datasets, employing metrics such as PSNR, SSIM, VFID, and $E_{warp}$ to quantify performance. The proposed framework demonstrated substantial improvements over state-of-the-art methods in both accuracy and efficiency. Specifically, E$^2$FGVI surpassed previous methods by achieving better distortion and perceptual similarity metrics, while also excelling in temporal consistency with a marked reduction in warping errors.

• Accuracy: The PSNR and SSIM scores achieved highlight the framework's dominance in generating high-fidelity reconstructions.
• Efficiency: Processing videos at 0.12 seconds per frame reflects a significant speed enhancement, approximately 15 times faster than traditional flow-based methods.

Implications

The advancement presented by E$^2$FGVI in integrating flow computations into an end-to-end learning framework harbors significant implications for both theoretical exploration and practical applications in video processing. The alleviation of traditional bottlenecks—such as error accumulation and inefficient manual operations—potentially sets a new standard for video inpainting tasks.

The paper posits E$^2$FGVI as a robust baseline for future development in the video inpainting domain. It suggests promising directions in which the integration of trainable components into holistic systems could be further exploited in other video-related tasks. The refined balance between efficiency and accuracy demonstrated also underscores potential applicability in real-time video processing scenarios, offering a compelling tool for applications such as frame restoration and object removal.

Future Directions

Further pursuits could delve into optimizing the transformer components or exploring alternative mechanisms for feature-level operations. The framework's adaptability and performance indicate that its concepts could influence broader applications beyond video inpainting, possibly extending to enhanced video analysis or synthesis tasks.

This paper provides a detailed, well-structured approach to tackle longstanding challenges in video inpainting, delivering a high-impact contribution to the field.