For a given defect video, the difficulty of video inpainting lies in how to maintain the space–time continuity after filling the defect area and form a smooth and natural repaired result. An Internal Learning Approach to Video Inpainting International Conference on Computer Vision (ICCV) 2019 Published October 28, 2019 Haotian Zhang, Long Mai, Ning Xu, Zhaowen Wang, John Collomosse, Hailin Jin Haotian Zhang. lengthy meta-learning on a large dataset of videos, and af-ter that is able to frame few- and one-shot learning of neural talking head models of previously unseen people as adver- sarial training problems with high capacity generators and discriminators. warp.2) $1 - M_{i,j}^f$. (2019) Various Approaches for Video Inpainting: A Survey. In a nutshell, the contributions of the present paper are as follows: { We show that a mask-speci c inpainting method can be learned with neural An Internal Learning Approach to Video Inpainting International Conference on Computer Vision (ICCV) 2019 Published October 28, 2019 Haotian Zhang, Long … An Internal Learning Approach to Video Inpainting ... we want to adopt this curriculum learning approach for other computer vision tasks, including super-resolution and de-blurring. Video inpainting aims to restore missing regions of a video and has many applications such as video editing and object removal. Mark. Although learning image priors from an external image corpus via a deep neural network can improve image inpainting performance, extending neural networks to video inpainting remains challenging because the hallucinated content in videos not only needs to be consistent within its own frame, but also across adjacent frames. This paper proposes a new approach of video inpainting technology to detect and restore damaged films. They are also able to do blind inpainting (as we do in Sec. Haotian Zhang. arXiv preprint arXiv:1701.07875. Video inpainting has also been used as a self-supervised task for deep feature learning [32] which has a different goal from ours. Copy-and-Paste Networks for Deep Video Inpainting : Video: 2019: ICCV 2019: Onion-Peel Networks for Deep Video Completion : Video: 2019: ICCV 2019: Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN : Video: 2019: ICCV 2019: An Internal Learning Approach to Video Inpainting : Video: 2019: ICCV 2019 Proposal-based Video Completion Yuan-Ting Hu1, Heng Wang2, Nicolas Ballas3, Kristen Grauman3;4, and Alexander G. Schwing1 1University of Illinois Urbana-Champaign 2Facebook AI 3Facebook AI Research 4University of Texas at Austin Abstract. A New Approach with Machine Learning. 2720-2729, 2019. from frame $I_i$ to frame $I_j$.2) $M^f_{i,j} = M_i \cap M_j (F_{i,j})$. We take a generative approach to inpainting based on internal (within-video) learning without reliance upon an external corpus of visual data to train a one-size-fits-all model for the large space of general videos. State-of-the-art approaches adopt attention models to complete a frame by searching missing contents from reference frames, and further complete whole videos … As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. References [1] M . The noise map Ii has one channel and shares the same spatial size with the input frame. Video inpainting is an important technique for a wide vari-ety of applications from video content editing to video restoration. 1) $F_{i,j}$. We propose the first deep learning solution to video frame inpainting, a challenging instance of the general video inpainting problem with applications in video editing, manipulation, and forensics. Please contact me ([email protected]) if you find any interesting paper about inpainting that I missed.I would greatly appreciate it : ) I'm currently busy on some other projects. arXiv preprint arXiv:1909.07957, 2019. (2019) An Internal Learning Approach to Video Inpainting. We present a new data-driven video inpainting method for recovering missing regions of video frames. The generative network \(G_{\theta}\) is trained to predict both frames \(\hat{I}_i\) and optical flow maps \(\hat{F}_{i,i\pm t}\). Cited by: §1. An Internal Learning Approach to Video Inpainting Haotian Zhang, Long Mai, Ning Xu, Zhaowen Wang, John Collomosse, Hailin Jin. The general idea is to use the input video as the training data to learn a generative neural network \(G_{\theta}\) to generate each target frame \(I^*_i\) from a corresponding noise map \(N_i\). We take a generative approach to inpainting based on internal (within-video) learning without reliance upon an external corpus of visual data to train a one-size-fits-all model for the large space of general videos. arXiv preprint arXiv:1909.07957, 2019. Our work is inspired by the recent ‘Deep Image Prior’ (DIP) work by Ulyanov et al. [40] Motivation & Design. In ECCV2020 High-quality video inpainting that completes missing regions in video frames is a promising yet challenging task. Applications from video content editing to video inpainting [ J ] Laparoscopic-Hysteroscopic Isthmoplasty using the Rendez-vous technique Step! ( CVPR 2016 ) you Only Look Once: Unified, Real-Time Detection! Yet challenging task as thin scratches ( as we do in Sec deep image Prior ’ DIP. 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