Video Inpainting of Complex Scenes

We propose an automatic video inpainting algorithm which relies on the optimisation of a global, patch-based functional. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background. Furthermore, we achieve this in an order of magnitude less execution time with respect to the state-of-the-art. We are also able to achieve good quality results on high definition videos. Finally, we provide specific algorithmic details to make implementation of our algorithm as easy as possible. The resulting algorithm requires no segmentation or manual input other than the definition of the inpainting mask, and can deal with a wider variety of situations than is handled by previous work. 1. Introduction. Advanced image and video editing techniques are increasingly common in the image processing and computer vision world, and are also starting to be used in media entertainment. One common and difficult task closely linked to the world of video editing is image and video " inpainting ". Generally speaking, this is the task of replacing the content of an image or video with some other content which is visually pleasing. This subject has been extensively studied in the case of images, to such an extent that commercial image inpainting products destined for the general public are available, such as Photoshop's " Content Aware fill " [1]. However, while some impressive results have been obtained in the case of videos, the subject has been studied far less extensively than image inpainting. This relative lack of research can largely be attributed to high time complexity due to the added temporal dimension. Indeed, it has only very recently become possible to produce good quality inpainting results on high definition videos, and this only in a semi-automatic manner. Nevertheless, high-quality video inpainting has many important and useful applications such as film restoration, professional post-production in cinema and video editing for personal use. For this reason, we believe that an automatic, generic video inpainting algorithm would be extremely useful for both academic and professional communities.

[1]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.

[2]  Eli Shechtman,et al.  Image melding , 2012, ACM Trans. Graph..

[3]  Patrick Pérez,et al.  Object removal by exemplar-based inpainting , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[4]  Shai Avidan,et al.  Coherency Sensitive Hashing , 2011, ICCV.

[5]  Tai-Pang Wu,et al.  Video repairing under variable illumination using cyclic motions , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Richard Szeliski,et al.  Video textures , 2000, SIGGRAPH.

[7]  Yael Pritch,et al.  Shift-map image editing , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  T. Chan,et al.  Image inpainting by correspondence maps: A deterministic approach , 2003 .

[9]  Wolfgang Broll,et al.  PixMix: A real-time approach to high-quality Diminished Reality , 2012, 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

[10]  Guillermo Sapiro,et al.  A Comprehensive Framework for Image Inpainting , 2010, IEEE Transactions on Image Processing.

[11]  Nikos Komodakis,et al.  Image Completion Using Efficient Belief Propagation Via Priority Scheduling and Dynamic Pruning , 2007, IEEE Transactions on Image Processing.

[12]  Jean-Marc Odobez,et al.  Robust Multiresolution Estimation of Parametric Motion Models , 1995, J. Vis. Commun. Image Represent..

[13]  Jian Sun,et al.  Statistics of Patch Offsets for Image Completion , 2012, ECCV.

[14]  Shai Avidan,et al.  TreeCANN - k-d Tree Coherence Approximate Nearest Neighbor Algorithm , 2012, ECCV.

[15]  Vicent Caselles,et al.  Exemplar-Based Image Inpainting Using Multiscale Graph Cuts , 2013, IEEE Transactions on Image Processing.

[16]  Adam Finkelstein,et al.  PatchMatch: a randomized correspondence algorithm for structural image editing , 2009, SIGGRAPH 2009.

[17]  Sunil Arya,et al.  Approximate nearest neighbor queries in fixed dimensions , 1993, SODA '93.

[18]  Mark J. Huiskes,et al.  DynTex: A comprehensive database of dynamic textures , 2010, Pattern Recognit. Lett..

[19]  Jean-Michel Morel,et al.  Level lines based disocclusion , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[20]  Louis Laborelli,et al.  Missing data correction in still images and image sequences , 2002, MULTIMEDIA '02.

[21]  Jan Kautz,et al.  Background Inpainting for Videos with Dynamic Objects and a Free-Moving Camera , 2012, ECCV.

[22]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[23]  Sung Yong Shin,et al.  On pixel-based texture synthesis by non-parametric sampling , 2006, Comput. Graph..

[24]  Jian Zhao,et al.  Efficient Object-Based Video Inpainting , 2006, 2006 International Conference on Image Processing.

[25]  Yong-Sheng Chen,et al.  Virtual Contour Guided Video Object Inpainting Using Posture Mapping and Retrieval , 2011, IEEE Transactions on Multimedia.

[26]  Eli Shechtman,et al.  Space-time video completion , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[27]  Guillermo Sapiro,et al.  Video Inpainting Under Constrained Camera Motion , 2007, IEEE Transactions on Image Processing.

[28]  Guillermo Sapiro,et al.  A Variational Framework for Exemplar-Based Image Inpainting , 2011, International Journal of Computer Vision.

[29]  Daniel Cohen-Or,et al.  Fragment-based image completion , 2003, ACM Trans. Graph..

[30]  Guillermo Sapiro,et al.  Video inpainting of occluding and occluded objects , 2005, IEEE International Conference on Image Processing 2005.

[31]  Irfan A. Essa,et al.  Graphcut textures: image and video synthesis using graph cuts , 2003, ACM Trans. Graph..

[32]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[33]  Guillermo Sapiro,et al.  Image inpainting , 2000, SIGGRAPH.

[34]  Jian Sun,et al.  Computing nearest-neighbor fields via Propagation-Assisted KD-Trees , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Eli Shechtman,et al.  Space-Time Completion of Video , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Oliver Grau,et al.  How Not to Be Seen — Object Removal from Videos of Crowded Scenes , 2012, Comput. Graph. Forum.

[37]  Patrick Pérez,et al.  Towards fast, generic video inpainting , 2013, CVMP '13.