Advertisement

Automatic Speed Graph Generation for Predefined Camera Paths

  • Ferran Argelaguet
  • Carlos Andujar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6133)

Abstract

Predefined camera paths are a valuable tool for the exploration of complex virtual environments. The speed at which the virtual camera travels along different path segments is key for allowing users to perceive and understand the scene while maintaining their attention. Current tools for speed adjustment of camera motion along predefined paths, such as keyframing, interpolation types and speed curve editors provide the animators with a great deal of flexibility but offer little support for the animator to decide which speed is better for each point along the path. In this paper we address the problem of computing a suitable speed curve for a predefined camera path through an arbitrary scene. We strive at adapting speed along the path to provide non-fatiguing, informative, interestingness and concise animations. Key elements of our approach include a new metric based on optical flow for quantifying the amount of change between two consecutive frames, the use of perceptual metrics to disregard optical flow in areas with low image saliency, and the incorporation of habituation metrics to keep the user attention. We also present the results of a preliminary user-study comparing user response with alternative approaches for computing speed curves.

Keywords

Motion Sickness Camera Motion Path Segment Speed Curve Simulator Sickness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Elmqvist, N., Tsigas, P.: A taxonomy of 3D occlusion management techniques. In: Proceedings of the IEEE conference on virtual reality, pp. 51–58 (2007)Google Scholar
  2. 2.
    Christie, M., Olivier, P., Normand, J.: Camera Control in Computer Graphics. Computer Graphics Forum 27(8), 2197–2218 (2008)CrossRefGoogle Scholar
  3. 3.
    Mackinlay, J.D., Card, S.K., Robertson, G.G.: Rapid controlled movement through a virtual 3d workspace. In: ACM SIGGRAPH 1990, pp. 171–176 (1990)Google Scholar
  4. 4.
    Ware, C., Fleet, D.: Context sensitive flying interface. In: Proceedings of the 1997 symposium on Interactive 3D graphics. ACM, New York (1997)Google Scholar
  5. 5.
    Nieuwenhuisen, D., Kamphuis, A., Overmars, M.: High quality navigation in computer games. Science of Computer Programming 67(1), 91–104 (2007)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Prokop, T., Schubert, M., Berger, W.: Visual influence on human locomotion. Experimental Brain Research 114(1), 63–70 (1997)CrossRefGoogle Scholar
  7. 7.
    Dichgans, J., Brandt, T.: Visual-vestibular interaction and motion perception. Cerebral control of eye movements and motion perception, 327–338 (1972)Google Scholar
  8. 8.
    Riecke, B., Cunningham, D., Bulthoff, H.: Spatial updating in virtual reality: the sufficiency of visual information. Psychological Research 71(3), 298–313 (2007)CrossRefGoogle Scholar
  9. 9.
    Kennedy, R., Lane, N., Berbaum, K., Lilienthal, M.: Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness. The International Journal of Aviation Psychology 3(3), 203–220 (1993)CrossRefGoogle Scholar
  10. 10.
    Reason, J.: Motion sickness adaptation: a neural mismatch model. Journal of the Royal Society of Medicine 71(11), 819 (1978)Google Scholar
  11. 11.
    Kolasinski, E.: Simulator Sickness in Virtual Environments. US Army Research Institute for the Behavioral and Social Sciences (1995)Google Scholar
  12. 12.
    So, R., Ho, A., Lo, W.: A metric to quantify virtual scene movement for the study of cybersickness: Definition, implementation, and verification. Presence: Teleoperators & Virtual Environments 10(2), 193–215 (2001)CrossRefGoogle Scholar
  13. 13.
    Itti, L., Koch, C., Niebur, E., et al.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on pattern analysis and machine intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  14. 14.
    Yee, H., Pattanaik, S., Greenberg, D.: Spatiotemporal sensitivity and visual attention for efficient rendering of dynamic environments. ACM Transactions on Graphics (TOG) 20(1), 39–65 (2001)CrossRefGoogle Scholar
  15. 15.
    Longhurst, P., Debattista, K., Chalmers, A.: A GPU based saliency map for high-fidelity selective rendering. In: Proc. of the ACM Conference on Computer graphics, virtual reality, visualisation and interaction, Africa, p. 29 (2006)Google Scholar
  16. 16.
    LaViola Jr., J.J.: A discussion of cybersickness in virtual environments. SIGCHI Bulletin 32(1), 47–56 (2000)CrossRefGoogle Scholar
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ferran Argelaguet
    • 1
  • Carlos Andujar
    • 1
  1. 1.MOVING GroupUniversitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations