Inpainting of Wide-Baseline Multiple Viewpoint Video | IEEE Journals & Magazine | IEEE Xplore

Inpainting of Wide-Baseline Multiple Viewpoint Video


Abstract:

We describe a non-parametric algorithm for multiple-viewpoint video inpainting. Uniquely, our algorithm addresses the domain of wide baseline multiple-viewpoint video (MV...Show More

Abstract:

We describe a non-parametric algorithm for multiple-viewpoint video inpainting. Uniquely, our algorithm addresses the domain of wide baseline multiple-viewpoint video (MVV) with no temporal look-ahead in near real time speed. A Dictionary of Patches (DoP) is built using multi-resolution texture patches reprojected from geometric proxies available in the alternate views. We dynamically update the DoP over time, and a Markov Random Field optimisation over depth and appearance is used to resolve and align a selection of multiple candidates for a given patch, this ensures the inpainting of large regions in a plausible manner conserving both spatial and temporal coherence. We demonstrate the removal of large objects (e.g., people) on challenging indoor and outdoor MVV exhibiting cluttered, dynamic backgrounds and moving cameras.
Published in: IEEE Transactions on Visualization and Computer Graphics ( Volume: 26, Issue: 7, 01 July 2020)
Page(s): 2417 - 2428
Date of Publication: 24 December 2018

ISSN Information:

PubMed ID: 30582545

Funding Agency:

Citations are not available for this document.

1 Introduction

Multiple viewpoint video is becoming commonplace in film and music video production, where shots are captured using multiple, wide-spaced, synchronised cameras for later editing. Frequently it is desirable to edit such footage to remove objects; e.g., to remove an actor in a motion-capture suit to be later replaced with an animated virtual character.

Cites in Papers - |

Cites in Papers - IEEE (2)

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1.
Shruti S. Phutke, Subrahmanyam Murala, "Image Inpainting via Correlated Multi-Resolution Feature Projection", IEEE Transactions on Visualization and Computer Graphics, vol.30, no.9, pp.5953-5964, 2024.
2.
Yanhong Zeng, Jianlong Fu, Hongyang Chao, Baining Guo, "Aggregated Contextual Transformations for High-Resolution Image Inpainting", IEEE Transactions on Visualization and Computer Graphics, vol.29, no.7, pp.3266-3280, 2023.

Cites in Papers - Other Publishers (2)

1.
G. Gopichand, A. Vijaya Krishna, I. Ravi Prakash Reddy, D. Vandana, K. Ramana, P. Purshotham, "High-Resolution Image Inpainting Using Generative Adversarial Networks", Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing, pp.715, 2023.
2.
Shehzad Afzal, Sohaib Ghani, Mohamad Mazen Hittawe, Sheikh Faisal Rashid, Omar M. Knio, Markus Hadwiger, Ibrahim Hoteit, "Visualization and Visual Analytics Approaches for Image and Video Datasets: A Survey", ACM Transactions on Interactive Intelligent Systems, vol.13, no.1, pp.1, 2023.
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References

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