Recurrent Temporal Aggregation Framework for Deep Video Inpainting

Video inpainting aims to fill in spatio-temporal holes in videos with plausible content. Despite tremendous progress on deep learning-based inpainting of a single image, it is still challenging to extend these methods to video domain due to the additional time dimension. In this paper, we propose a...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2020-05, Vol.42 (5), p.1038-1052
Main Authors: Kim, Dahun, Woo, Sanghyun, Lee, Joon-Young, Kweon, In So
Format: Article
Language:English
Subjects:
Agglomeration
Cameras
Coders
Decoding
Encoders-Decoders
Image restoration
Machine learning
Semantics
Streaming media
Task analysis
Three-dimensional displays
video caption removal
video completion
video decaptioning
video editing
Video inpainting
video object removal
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Summary:Video inpainting aims to fill in spatio-temporal holes in videos with plausible content. Despite tremendous progress on deep learning-based inpainting of a single image, it is still challenging to extend these methods to video domain due to the additional time dimension. In this paper, we propose a recurrent temporal aggregation framework for fast deep video inpainting. In particular, we construct an encoder-decoder model, where the encoder takes multiple reference frames which can provide visible pixels revealed from the scene dynamics. These hints are aggregated and fed into the decoder. We apply a recurrent feedback in an auto-regressive manner to enforce temporal consistency in the video results. We propose two architectural designs based on this framework. Our first model is a blind video decaptioning network (BVDNet) that is designed to automatically remove and inpaint text overlays in videos without any mask information. Our BVDNet wins the first place in the ECCV Chalearn 2018 LAP Inpainting Competition Track 2: Video Decaptioning. Second, we propose a network for more general video inpainting (VINet) to deal with more arbitrary and larger holes. Video results demonstrate the advantage of our framework compared to state-of-the-art methods both qualitatively and quantitatively. The codes are available at https://github.com/mcahny/Deep-Video-Inpainting, and https://github.com/shwoo93/video_decaptioning.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2019.2958083