“Exploratory modeling with collaborative design spaces”
Conference:
Type(s):
Title:
- Exploratory modeling with collaborative design spaces
Session/Category Title: Hair & collaborative modeling
Presenter(s)/Author(s):
Moderator(s):
Abstract:
Enabling ordinary people to create high-quality 3D models is a long-standing problem in computer graphics. In this work, we draw from the literature on design and human cognition to better understand the design processes of novice and casual modelers, whose goals and motivations are often distinct from those of professional artists. The result is a method for creating exploratory modeling tools, which are appropriate for casual users who may lack rigidly-specified goals or operational knowledge of modeling techniques.Our method is based on parametric design spaces, which are often high dimensional and contain wide quality variations. Our system estimates the distribution of good models in a space by tracking the modeling activity of a distributed community of users. These estimates drive intuitive modeling tools, creating a self-reinforcing system that becomes easier to use as more people participate.We present empirical evidence that the tools developed with our method allow rapid creation of complex, high-quality 3D models by users with no specialized modeling skills or experience. We report analyses of usage patterns garnered throughout the year-long deployment of one such tool, and demonstrate the generality of the method by applying it to several design spaces.
References:
1. Adomavicius, G., and Tuzhilin, A. 2005. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Trans. on Knowledge and Data Engineering 17, 6, 734–749. Google ScholarDigital Library
2. Allen, B., Curless, B., and Popović, Z. 2003. The space of human body shapes: reconstruction and parameterization from range scans. In Proc. SIGGRAPH, ACM Press, 587–594. Google ScholarDigital Library
3. Ashikhmin, M., and Shirley, P. 2000. An anisotropic Phong BRDF model. Journal of Graphics Tools 5, 25–32. Google ScholarDigital Library
4. Bengio, Y., and Vincent, P. 2004. Locally weighted full co-variance gaussian density estimation. Cirano working papers, CIRANO, May.Google Scholar
5. Blanz, V., and Vetter, T. 1999. A morphable model for the synthesis of 3D faces. In Proc. SIGGRAPH, ACM Press, 187–194. Google ScholarDigital Library
6. Brown, D., and Chandrasekaran, B. 1989. Design problem solving: knowledge structures and control strategies. Morgan Kaufmann, San Francisco, CA, USA. Google ScholarDigital Library
7. Buxton, B. 2007. Sketching User Experiences: Getting the Design Right and the Right Design. Morgan Kaufmann, April. Google ScholarDigital Library
8. Feder, T., and Greene, D. 1988. Optimal algorithms for approximate clustering. In STOC ’88: Proceedings of the twentieth annual ACM symposium on Theory of computing, ACM, New York, NY, USA, 434–444. Google ScholarDigital Library
9. Funkhouser, T., Kazhdan, M., Shilane, P., Min, P., Kiefer, W., Tal, A., Rusinkiewicz, S., and Dobkin, D. 2004. Modeling by example. In Proc. SIGGRAPH, ACM, 652–663. Google ScholarDigital Library
10. Furukawa, Y., and Ponce, J. 2007. Accurate, dense, and robust multi-view stereopsis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–8.Google Scholar
11. Gatz, D. F., and Smith, L. 1995. The standard error of a weighted mean concentration. Atmospheric Environment 29, 11, 1185–1193.Google ScholarCross Ref
12. Gero, J. 1990. Design prototypes: A knowledge representation schema for design. AI Magazine 11, 4. Google ScholarDigital Library
13. Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Commun. ACM 35, 12, 61–70. Google ScholarDigital Library
14. Hays, J., and Efros, A. A. 2007. Scene completion using millions of photographs. In Proc. SIGGRAPH, ACM Press, 4. Google ScholarDigital Library
15. Hoschek, J., and Dankwort, W. 1994. Parametric and Variational Design. B. G. Teubner.Google Scholar
16. Igarashi, T., and Hughes, J. F. 2001. A suggestive interface for 3D drawing. In Proc. UIST, ACM Press, 173–181. Google ScholarDigital Library
17. Igarashi, T., Matsuoka, S., and Tanaka, H. 1999. Teddy: a sketching interface for 3D freeform design. In Proc. SIGGRAPH, ACM Press, 409–416. Google ScholarDigital Library
18. Karpenko, O. A., and Hughes, J. F. 2006. Smoothsketch: 3D free-form shapes from complex sketches. In Proc. SIGGRAPH, ACM Press, 589–598. Google ScholarDigital Library
19. Kirsh, D., and Maglio, P. 1994. On distinguishing epistemic from pragmatic action. Cognitive Science 18, 4, 513–549.Google ScholarCross Ref
20. Kolodner, J. L., and Wills, L. M. 1993. Case-based creative design. In In AAAI Spring Symposium on AI and Creativity, 50–57.Google Scholar
21. Kovar, L., and Gleicher, M. 2001. Simplicial families of drawings. In Proceedings of the 14th annual ACM symposium on User interface software and technology, ACM, New York, NY, USA, 163–172. Google ScholarDigital Library
22. Lalonde, J.-F., Hoiem, D., Efros, A. A., Rother, C., Winn, J., and Criminisi, A. 2007. Photo clip art. In Proc. SIGGRAPH, ACM, New York, NY, USA, 3–13. Google ScholarDigital Library
23. Linden, G., Smith, B., and York, J. 2003. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7, 1, 76–80. Google ScholarDigital Library
24. MacEachern, A. 1994. Visualization in modern cartography: setting the agenda. Pergamon.Google Scholar
25. Marino, P. 2004. 3D Game-Based Filmmaking: The Art of Machinima. Paraglyph.Google Scholar
26. Marks, J., Andalman, B., Beardsley, P. A., Freeman, W., Gibson, S., Hodgins, J., Kang, T., Mirtich, B., Pfister, H., Ruml, W., Ryall, K., Seims, J., and Shieber, S. 1997. Design galleries: a general approach to setting parameters for computer graphics and animation. In Proc. SIGGRAPH, ACM Press, 389–400. Google ScholarDigital Library
27. Matusik, W., Pfister, H., Brand, M., and McMillan, L. 2003. A data-driven reflectance model. In Proc. SIGGRAPH, ACM Press, 759–769. Google ScholarDigital Library
28. Maxis Software. 2008. Spore. http://www.spore.com.Google Scholar
29. Miller, G. 2007. The promise of parallel universes. Science 317, 1341–1343.Google ScholarCross Ref
30. Navinchandra, D. 1991. Exploration and innovation in design: towards a computational model. Springer-Verlag, New York, NY, USA. Google ScholarDigital Library
31. Nealen, A., Igarashi, T., Sorkine, O., and Alexa, M. 2007. Fibermesh: designing freeform surfaces with 3D curves. In Proc. SIGGRAPH, ACM Press, 41–49. Google ScholarDigital Library
32. Newman, G. 2006. Garry’s Mod software, version 10. http://www.garrysmod.com.Google Scholar
33. Ngan, A., Durand, F., and Matusik, W. 2006. Image-driven navigation of analytical BRDF models. In Proceedings of the Eurographics Symposium on Rendering, 389–400. Google ScholarCross Ref
34. Nintendo Company Ltd. 2006. Mii Channel. http://us.wii.com/.Google Scholar
35. Parke, F. I. 1974. A parametric model for human faces. Tech. rep., University of Utah.Google Scholar
36. Parzen, E. 1962. On estimation of a probability density function and mode. Annals of mathematical statistics 33, 1065–1076.Google ScholarCross Ref
37. Raskutti, B., and Leckie, C. 1999. An evaluation of criteria for measuring the quality of clusters. In Proc. International Joint Conference on Artificial Intelligence, Morgan Kaufmann, 905–910. Google ScholarDigital Library
38. Robert, C. P. 1995. Simulation of truncated normal variables. Statistics and Computing 5, 121–125.Google ScholarCross Ref
39. Schäfer, J., and Strimmer, K. 2005. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology 4, 1, 1–32.Google ScholarCross Ref
40. Scott, D. W., and Sain, S. R. 2004. Multi-dimensional density estimation. Data Mining and Computational Statistics 23.Google Scholar
41. Shapira, L., Shamir, A., and Cohen-Or, D. 2009. Image appearance exploration by model-based navigation. Computer Graphics Forum 28, 2, 629–638.Google ScholarCross Ref
42. Snavely, N., Seitz, S. M., and Szeliski, R. 2006. Photo tourism: exploring photo collections in 3D. In Proc. SIGGRAPH, ACM Press, 835–846. Google ScholarDigital Library
43. Su, Q., Pavlov, D., Chow, J.-H., and Baker, W. C. 2007. Internet-scale collection of human-reviewed data. In WWW ’07: Proceedings of the 16th international conference on World Wide Web, ACM, New York, NY, USA, 231–240. Google ScholarDigital Library
44. Tsang, S., Balakrishnan, R., Singh, K., and Ranjan, A. 2004. A suggestive interface for image guided 3d sketching. In Proc. of CHI, ACM Press, 591–598. Google ScholarDigital Library
45. Tversky, B., Agrawala, M., Heiser, J., Lee, P., Hanrahan, P., Phan, D., Stolte, C., and Daniel, M.-P. 2007. Cognitive design principles for generating visualizations. In Applied spatial cognition: from research to cognitive technology, 55–73.Google Scholar
46. Tweedie, L. 1995. Interactive visualisation artifacts: how can abstractions inform design? In HCI ’95: Proceedings of the HCI’95 conference on People and Computers, Cambridge University Press, 247–265. Google ScholarDigital Library
47. van den Hengel, A., Dick, A., Thormählen, T., Ward, B., and Torr, P. H. S. 2007. Videotrace: rapid interactive scene modelling from video. In Proc. SIGGRAPH, ACM Press, 86–91. Google ScholarDigital Library
48. Visser, W. 2006. The Cognitive Artifacts of Designing. L. Erlbaum Associates Inc., Hillsdale, NJ, USA. Google ScholarDigital Library
49. von Ahn, L., and Dabbish, L. 2004. Labeling images with a computer game. In CHI ’04: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, New York, NY, USA, 319–326. Google ScholarDigital Library
50. Weber, J., and Penn, J. 1995. Creation and rendering of realistic trees. In Proc. SIGGRAPH, ACM Press, 119–128. Google ScholarDigital Library
