Abstract
Video texture is an appealing method to extract and replay natural human motion from video shots. There have been much research on video texture analysis, generation and interactive control. However, the video sprites created by existing methods are typically restricted to constant depths, so that the motion diversity is strongly limited. In this paper, we propose a novel depth-varying human video sprite synthesis method, which significantly increases the degrees of freedom of human video sprite. A novel image distance function encoding scale variation is proposed, which can effectively measure the human snapshots with different depths/scales and poses, so that aligning similar poses with different depths is possible. The transitions among non-consecutive frames are modeled as a 2D transformation matrix, which can effectively avoid drifting without leveraging markers or user intervention. The synthesized depth-varying human video sprites can be seamlessly inserted into new scenes for realistic video composition. A variety of challenging examples demonstrate the effectiveness of our method.
This work was supported by the 973 program of China (No. 2009CB320802), NSF of China (Nos. 60633070 and 60903135), China Postdoctoral Science Foundation funded project(No. 20100470092).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arikan, O., Forsyth, D.A.: Interactive motion generation from examples. In: SIGGRAPH 2002: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, pp. 483–490. ACM, New York (2002)
Bai, X., Wang, J., Simons, D., Sapiro, G.: Video snapcut: robust video object cutout using localized classifiers. In: SIGGRAPH 2009: ACM SIGGRAPH 2009 Papers, pp. 1–11. ACM, New York (2009)
Beier, T., Neely, S.: Feature-based image metamorphosis. In: Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1992, pp. 35–42. ACM, New York (1992), http://doi.acm.org/10.1145/133994.134003
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)
Berg, A.C., Malik, J.: Geometric blur for template matching, vol. 1, p. 607. IEEE Computer Society, Los Alamitos (2001)
Celly, B., Zordan, V.B.: Animated people textures. In: Proceedings of 17th International Conference on Computer Animation and Social Agents (CASA), Citeseer (2004)
Flagg, M., Nakazawa, A., Zhang, Q., Kang, S.B., Ryu, Y.K., Essa, I., Rehg, J.M.: Human video textures. In: SI3D 2009: Proceedings of the 2009 Symposium on Interactive 3D Graphics and Games (2009)
Kovar, L., Gleicher, M., Pighin, F.: Motion graphs. In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2002, pp. 473–482. ACM, New York (2002), http://doi.acm.org/10.1145/566570.566605
Lee, J., Chai, J., Reitsma, P.S.A., Hodgins, J.K., Pollard, N.S.: Interactive control of avatars animated with human motion data. In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2002, pp. 491–500. ACM, New York (2002), http://doi.acm.org/10.1145/566570.566607
Li, Y., Wang, T., Shum, H.Y.: Motion texture: a two-level statistical model for character motion synthesis. In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2002, pp. 465–472. ACM, New York (2002), http://doi.acm.org/10.1145/566570.566604
Liu, C., Freeman, W.T., Adelson, E.H., Weiss, Y.: Human-assisted motion annotation. In: CVPR (2008)
Mori, G., Berg, A., Efros, A., Eden, A.: Video based motion synthesis by splicing and morphing. Tech. rep., University of California, Berkeley (2004)
Niebles, J.C., Chen, C.-W., Fei-Fei, L.: Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 392–405. Springer, Heidelberg (2010)
Schödl, A., Essa, I.: Machine learning for video-based rendering. In: Advances in Neural Information Processing Systems, pp. 1002–1008. MIT Press (2000)
Schödl, A., Essa, I.A.: Controlled animation of video sprites. In: SCA 2002: Proceedings of the 2002 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 121–127. ACM, New York (2002)
Schödl, A., Szeliski, R., Salesin, D.H., Essa, I.: Video textures. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2000, pp. 489–498. ACM Press/Addison-Wesley Publishing Co., New York, NY (2000), http://dx.doi.org/10.1145/344779.345012
Xu, X., Wan, L., Liu, X., Wong, T.-T., Wang, L., Leung, C.-S.: Animating animal motion from still. ACM Trans. Graph. 27(5), 1–8 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hua, W., Yang, W., Dong, Z., Zhang, G. (2012). Depth-Varying Human Video Sprite Synthesis. In: Pan, Z., Cheok, A.D., Müller, W., Chang, M., Zhang, M. (eds) Transactions on Edutainment VII. Lecture Notes in Computer Science, vol 7145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29050-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-29050-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29049-7
Online ISBN: 978-3-642-29050-3
eBook Packages: Computer ScienceComputer Science (R0)