Flow and Color Inpainting for Video Completion

Strobel, Michael; Diebold, Julia; Cremers, Daniel

doi:10.1007/978-3-319-11752-2_23

Cited by 28 publications

(16 citation statements)

References 21 publications

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“…We apply the image inpainting method based on fast marching [15] as done in [17] for video completion, which is a different goal than ours. We similarly extend the Navier-Stokes based image inpainting method of [16] to flow inpainting.…”

Section: Optical Flow Inpaintingmentioning

confidence: 99%

Unsupervised Motion Saliency Map Estimation Based On Optical Flow Inpainting

Maczyta

Bouthémy

Meur³

2019

2019 IEEE International Conference on Image Processing (ICIP)

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The paper addresses the problem of motion saliency in videos, that is, identifying regions that undergo motion departing from its context. We propose a new unsupervised paradigm to compute motion saliency maps. The key ingredient is the flow inpainting stage. Candidate regions are determined from the optical flow boundaries. The residual flow in these regions is given by the difference between the optical flow and the flow inpainted from the surrounding areas. It provides the cue for motion saliency. The method is flexible and general by relying on motion information only. Experimental results on the DAVIS 2016 benchmark demonstrate that the method compares favourably with state-of-theart video saliency methods.

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Section: Optical Flow Inpaintingmentioning

confidence: 99%

Unsupervised Motion Saliency Map Estimation Based On Optical Flow Inpainting

Maczyta

Bouthémy

Meur³

2019

2019 IEEE International Conference on Image Processing (ICIP)

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“…Inpainting real-world high-definition video sequences remains challenging due to the camera motion and the complex movement of objects. Most existing video inpainting algorithms [12,21,22,27,30] follow the traditional image inpainting pipeline, by formulating the problem as a patch-based optimization task, which fills missing regions through sampling spatial or spatial-temporal patches of the known regions then solve minimization problem. Despite some good results, these approaches suffer from two draw-backs.…”

Section: Introductionmentioning

confidence: 99%

Deep Flow-Guided Video Inpainting

Zhou

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

244

196

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Video inpainting, which aims at filling in missing regions of a video, remains challenging due to the difficulty of preserving the precise spatial and temporal coherence of video contents. In this work we propose a novel flow-guided video inpainting approach. Rather than filling in the RGB pixels of each frame directly, we consider video inpainting as a pixel propagation problem. We first synthesize a spatially and temporally coherent optical flow field across video frames using a newly designed Deep Flow Completion network. Then the synthesized flow field is used to guide the propagation of pixels to fill up the missing regions in the video. Specifically, the Deep Flow Completion network follows a coarse-to-fine refinement to complete the flow fields, while their quality is further improved by hard flow example mining. Following the guide of the completed flow, the missing video regions can be filled up precisely. Our method is evaluated on DAVIS and YouTube-VOS datasets qualitatively and quantitatively, achieving the state-of-the-art performance in terms of inpainting quality and speed. The project page is available at https: //nbei.github.io/video-inpainting.html

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“…The motion inpainting problem has been addressed previously in the literature for different purposes. In order to inpaint the flow in the occlusion areas, Matsushita et al [17] and Strobel et al [26] extended the Telea-inpainting method [27] to optical flow, i.e., they assume that the motion variation is locally small and propagate the optical flow according to a weighting function which depends on the Euclidean distance and the color difference among the interpolated pixel and its neighbours. Kondermann et al [13] proposed a postprocess of the optical flow in order to improve it: the optical flow is retained at points where it is reliable and is then densified by minimizing the L 2 norm of the spatio-temporal gradient of the flow.…”

Section: Introductionmentioning

confidence: 99%

Motion Inpainting by an Image-Based Geodesic AMLE Method

Oliver

Raad

Ballester

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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This work presents an automatic method for optical flow inpainting. Given a video, each frame domain is endowed with a Riemannian metric based on the video pixel values. The missing optical flow is recovered by solving the Absolutely Minimizing Lipschitz Extension (AMLE) partial differential equation on the Riemannian manifold. An efficient numerical algorithm is proposed using eikonal operators for nonlinear elliptic partial differential equations on a finite graph. The choice of the metric is discussed and the method is applied to optical flow inpainting and sparse-to-dense optical flow estimation, achieving top-tier performance in terms of End-Point-Error (EPE).

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Flow and Color Inpainting for Video Completion

Cited by 28 publications

References 21 publications

Unsupervised Motion Saliency Map Estimation Based On Optical Flow Inpainting

Unsupervised Motion Saliency Map Estimation Based On Optical Flow Inpainting

Deep Flow-Guided Video Inpainting

Motion Inpainting by an Image-Based Geodesic AMLE Method

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