Deep Video Inpainting (1905.01639v1)

Abstract: Video inpainting aims to fill spatio-temporal holes with plausible content in a video. Despite tremendous progress of deep neural networks for image inpainting, it is challenging to extend these methods to the video domain due to the additional time dimension. In this work, we propose a novel deep network architecture for fast video inpainting. Built upon an image-based encoder-decoder model, our framework is designed to collect and refine information from neighbor frames and synthesize still-unknown regions. At the same time, the output is enforced to be temporally consistent by a recurrent feedback and a temporal memory module. Compared with the state-of-the-art image inpainting algorithm, our method produces videos that are much more semantically correct and temporally smooth. In contrast to the prior video completion method which relies on time-consuming optimization, our method runs in near real-time while generating competitive video results. Finally, we applied our framework to video retargeting task, and obtain visually pleasing results.

• The paper introduces a novel deep network that integrates a 3D-2D encoder-decoder with recurrent memory to achieve consistent video inpainting.
• It presents robust temporal feature aggregation by synthesizing frames with feedback loops and flow-based loss mechanisms.
• Experimental results show significant improvements in temporal consistency and visual quality compared to previous video restoration methods.

Deep Video Inpainting: A Novel Method for Achieving Temporal Consistency in Video Restoration

The paper Deep Video Inpainting presents a sophisticated approach to video inpainting—addressing the challenge of filling spatio-temporal voids within video frames through plausible content synthesis. This process is crucial for applications such as object removal, damage restoration, and video editing. Unlike the well-trodden ground of image inpainting, video inpainting contends with the additional complexity of temporal coherence across a sequence of frames. The authors propose a unique deep learning framework that effectively marries spatial and temporal consistency in video restoration.

Proposed Methodology

The authors introduce an innovative deep network architecture that evolves beyond existing image-based encoder-decoder models to accommodate video data. This method involves a 3D-2D encoder-decoder network, specifically tailored for video inpainting. The multi-to-single frame framework is designed for two core functions: temporal feature aggregation and preservation of temporal consistency. By adopting a recurrent feedback loop and incorporating a memory module, the proposed system ensures that the synthesized content remains stable over time.

Key features of the proposed architecture include:

• Temporal Feature Aggregation: The task of video inpainting is approached as a sequential process, synthesizing each frame based on aggregated features from its neighbors. This approach contrasts with naive frame-by-frame inpainting that neglects the temporal dimension.
• Temporal Consistency: Temporal stability is achieved through the use of recurrent feedback mechanisms and a ConvLSTM memory layer. Additionally, a flow loss and a warping loss are employed to mitigate temporal discrepancies.

The architecture shows impressive results when augmented to tasks like video retargeting, where the system maintains important visual content while modifying the frame's aspect ratio.

Experiments and Results

Extensive experiments validate the effectiveness of the proposed method, with significant improvements in temporal consistency and visual quality metrics over previous techniques. The authors compare their technique with state-of-the-art methods such as Yu's feed-forward single-image inpainting and Huang's optimization-based video completion. Both quantitative metrics like flow warping errors and perceptual metrics like FID scores demonstrate superior performance by the proposed method.

Moreover, through a series of subjective evaluations, the method's results were preferred substantially over those by traditional optimization approaches, indicating its effectiveness in real-world video editing scenarios. This shows the model's potential beyond academic evaluation, urging its application in practical settings such as video content creation and augmented reality tasks.

Implications and Future Directions

This research advances the potential of deep learning in video restoration, setting new benchmarks for temporal consistency and speed. The development bridges a critical gap left by earlier inpainting techniques, providing a unified model with applications in a variety of video editing contexts. Through efficient processing enabled by a feed-forward network, this approach sidesteps the traditional bottlenecks associated with optimization-intensive methods.

For AI researchers and practitioners, this method paves the way for further research into temporal dynamics in video data, highlighting the need for efficient and temporally stable solutions. Future work could explore scaling this approach to higher resolutions or extending it for real-time video applications or other domains requiring temporal continuity.

The Deep Video Inpainting paper contributes to the field of computer vision by providing a robust solution that aligns with the growing demands for high-quality and temporally coherent video processing.