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Deep Video Inpainting

2019-05-05 09:23:35
Dahun Kim, Sanghyun Woo, Joon-Young Lee, In So Kweon

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.

Abstract (translated)

视频输入旨在用视频中看似合理的内容填补时空空白。尽管深层神经网络在图像修复方面取得了巨大的进展,但由于时间维度的增加,将这些方法扩展到视频领域是一个挑战。在这项工作中,我们提出了一种新的深层网络架构,用于快速视频修复。基于基于图像的编码器-解码器模型,我们的框架设计用于从相邻帧收集和优化信息,并合成仍然未知的区域。同时,通过循环反馈和时间记忆模块强制输出暂时一致。与目前最先进的图像修复算法相比,我们的方法生成的视频在语义上更加正确,在时间上更加流畅。与以往的基于耗时优化的视频完成方法相比,我们的方法在产生竞争性视频结果的同时几乎实时运行。最后,将该框架应用于视频重定目标任务,取得了令人满意的视觉效果。

URL

https://arxiv.org/abs/1905.01639

PDF

https://arxiv.org/pdf/1905.01639.pdf


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