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Temporal Priors for Novel Video Synthesis

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Computer Vision – ACCV 2007 (ACCV 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4844))

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Abstract

In this paper we propose a method to construct a virtual sequence for a camera moving through a static environment given an input sequence from a different camera trajectory. Existing image-based rendering techniques can generate photorealistic images given a set of input views, though the output images almost unavoidably contain small regions where the colour has been incorrectly chosen. In a single image these artifacts are often hard to spot, but become more obvious when viewing a real image with its virtual stereo pair, and even more so when when a sequence of novel views is generated, since the artifacts are rarely temporally consistent.

To address this problem of consistency, we propose a new spatio-temporal approach to novel video synthesis. The pixels in the output video sequence are modelled as nodes of a 3–D graph. We define an MRF on the graph which encodes photoconsistency of pixels as well as texture priors in both space and time. Unlike methods based on scene geometry which yield highly connected graphs, our approach results in a graph whose degree is independent of scene structure. The MRF energy is therefore tractable and we solve it for the whole sequence using a state-of-the-art message passing optimisation algorithm. We demonstrate the effectiveness of our approach in reducing temporal artifacts.

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References

  1. Okutomi, M., Kanade, T.: A multiple-baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(4), 353–363 (1993)

    Article  Google Scholar 

  2. Strecha, C., Fransens, R., Gool, L.V.: Combined depth and outlier estimation in multi-view stereo. In: Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, pp. 2394–2401. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  3. Gargallo, P., Sturm, P.: Bayesian 3d modeling from images using multiple depth maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, California, vol. 2, pp. 885–891 (2005)

    Google Scholar 

  4. Goesele, M., Seitz, S.M., Curless, B.: Multi-View Stereo Revisited. In: Conference on Computer Vision and Pattern Recognition, New York, USA (2006)

    Google Scholar 

  5. Kutulakos, K., Seitz, S.: A Theory of Shape by Space Carving. International Journal of Computer Vision 38(3), 197–216 (2000)

    Article  Google Scholar 

  6. Kolmogorov, V., Zabih, R.: Multi-Camera Scene Reconstruction via Graph Cuts. In: European Conference on Computer Vision, Copenhagen, Denmark (2002)

    Google Scholar 

  7. Sun, J., Zheng, N., Shum, H.: Stereo matching using belief propagation. IEEE Transactions on Pattern Analysis 25, 1–14 (2003)

    Article  Google Scholar 

  8. Tappen, M., Freeman, W.: Comparison of graph cuts with belief propagation for stereo,using identical MRF parameters. In: International Conference on Computer Vision (2003)

    Google Scholar 

  9. Kolmogorov, V., Rother, C.: Comparison of energy minimization algorithms for highly connected graphs. In: European Conference on Computer Vision, Graz, Austria (2006)

    Google Scholar 

  10. Fitzgibbon, A., Wexler, Y., Zisserman, A.: Image-based rendering using image-based priors. In: Proceedings of the International Conference on Computer Vision, vol. 2, pp. 1176–1183 (2003)

    Google Scholar 

  11. Woodford, O.J., Reid, I.D., Fitzgibbon, A.W.: Efficient new view synthesis using pairwise dictionary priors. In: Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  12. Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1568–1583 (2006)

    Article  Google Scholar 

  13. Kohli, P., Kumar, M.P., Torr, P.H.: P3 & Beyond: Solving Energies with Higher Order Cliques. In: Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  14. Potetz, B.: Efficient Belief Propagation for Vision Using Linear Constraint Nodes. In: Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

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Yasushi Yagi Sing Bing Kang In So Kweon Hongbin Zha

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© 2007 Springer-Verlag Berlin Heidelberg

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Shahrokni, A., Woodford, O., Reid, I. (2007). Temporal Priors for Novel Video Synthesis. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_59

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  • DOI: https://doi.org/10.1007/978-3-540-76390-1_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76389-5

  • Online ISBN: 978-3-540-76390-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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