Visualization of 3D Video

  • Takashi Matsuyama
  • Shohei Nobuhara
  • Takeshi Takai
  • Tony Tung

Abstract

Visualization is one of the most standard applications of 3D video. Its essential functionality includes interactive free-viewpoint and 3D (pop-up) visualization of the captured scene as is. Following an ordinary 3D video visualization system, this chapter presents a novel free-viewpoint visualization method for 3D video stream of a single human in action. The novelty rests in that the 3D video is visualized from the performer’s viewpoint. Ordinary free-viewpoint visualization methods render the object action viewed from the outside of the scene. We may call it an objective, or third-person, view of the object action. With 3D video data, moreover, we can render a subjective, or first-person, view of the object action, where the object action is visualized as if it were captured from a head-mounted camera. Such subjective visualization is very useful to understand where to look when performing juggling or traditional dances; in MAIKO dances, for example, eye motions are very important to express mental feelings.

Keywords

Editing Suffix 

References

  1. 1.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001) CrossRefGoogle Scholar
  2. 2.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981) MathSciNetCrossRefGoogle Scholar
  3. 3.
    Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007) Google Scholar
  4. 4.
    Hansen, D.W., Ji, Q.: In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 478–500 (2010) CrossRefGoogle Scholar
  5. 5.
    Just, M.A., Carpenter, P.A.: Eye fixations and cognitive processes. Cogn. Psychol. 8(4), 441–480 (1976) CrossRefGoogle Scholar
  6. 6.
    Kawaguchi, T., Rizon, M., Hidaka, D.: Detection of eyes from human faces by hough transform and separability filter. Electron. Commun. Jpn. 88(5), 29–39 (2005) CrossRefGoogle Scholar
  7. 7.
    Kuroda, M., Nobuhara, S., Matsuyama, T.: 3d face geometry and gaze estimation from multi-view images using symmetry prior. In: Proc. of MIRU (2011) (in Japanese) Google Scholar
  8. 8.
    Nobuhara, S., Kimura, Y., Matsuyama, T.: Object-oriented color calibration of multi-viewpoint cameras in sparse and convergent arrangement. IPSJ Trans. Comput. Vis. Appl. 2, 132–144 (2010) Google Scholar
  9. 9.
    Nobuhara, S., Tsuda, Y., Ohama, I., Matsuyama, T.: Multi-viewpoint silhouette extraction with 3D context-aware error detection, correction, and shadow suppression. IPSJ Trans. Comput. Vis. Appl. 1, 242–259 (2009) Google Scholar
  10. 10.
    Sugimoto, A., Matsuyama, T.: Active wearable vision sensor: detecting person’s blink points and estimating human motion trajectory. In: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2003, AIM2003, vol. 1, pp. 539–545 (2003) CrossRefGoogle Scholar
  11. 11.
    Tobii Technology: X120 eye tracker Google Scholar
  12. 12.
    Tung, T., Nobuhara, S., Matsuyama, T.: Simultaneous super-resolution and 3D video using graph-cuts. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008) Google Scholar
  13. 13.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Takashi Matsuyama
    • 1
  • Shohei Nobuhara
    • 1
  • Takeshi Takai
    • 1
  • Tony Tung
    • 1
  1. 1.Graduate School of InformaticsKyoto UniversitySakyoJapan

Personalised recommendations