Recurrent Temporal Aggregation Framework for Deep Video Inpainting

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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.
Publisher
IEEE COMPUTER SOC
Issue Date
2020-05
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.42, no.5, pp.1038 - 1052

ISSN
0162-8828
DOI
10.1109/TPAMI.2019.2958083
URI
http://hdl.handle.net/10203/273952
Appears in Collection
EE-Journal Papers(저널논문)
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