Yuki Koyama
Daisuke Sakamoto
Takeo Igarashi
ABSTRACT
Parameter tweaking is one of the fundamental tasks in the editing of visual digital contents, such as correcting photo color or executing blendshape facial expression control. A problem with parameter tweaking is that it often requires much time and effort to explore a high-dimensional parameter space. We present a new technique to analyze such high-dimensional parameter space to obtain a distribution of human preference. Our method uses crowdsourcing to gather pairwise comparisons between various parameter sets. As a result of analysis, the user obtains a goodness function that computes the goodness value of a given parameter set. This goodness function enables two interfaces for exploration: Smart Suggestion, which provides suggestions of preferable parameter sets, and VisOpt Slider, which interactively visualizes the distribution of goodness values on sliders and gently optimizes slider values while the user is editing. We created four applications with different design parameter spaces. As a result, the system could facilitate the user's design exploration.
Supplemental Material
- Agrawal, S., Shen, S., and van de Panne, M. Diverse motion variations for physics-based character animation. In Proc. SCA '13, ACM (2013), 37--44. Google Scholar
Digital Library
- Bangor, A., Kortum, P. T., and Miller, J. T. An empirical evaluation of the system usability scale. Int. J. Hum.-Comput. Int. 24, 6 (2008), 574--594.Google ScholarCross Ref
- Bishop, C. M. Neural Networks for Pattern Recognition. Oxford University Press, Inc., 1995. Google ScholarDigital Library
- Carr, J. C., Beatson, R. K., Cherrie, J. B., Mitchell, T. J., Fright, W. R., McCallum, B. C., and Evans, T. R. Reconstruction and representation of 3d objects with radial basis functions. In Proc. SIGGRAPH '01, ACM (2001), 67--76. Google ScholarDigital Library
- Chaudhuri, S., Kalogerakis, E., Giguere, S., and Funkhouser, T. Attribit: Content creation with semantic attributes. In Proc. UIST '13, ACM (2013), 193--202. Google ScholarDigital Library
- Gingold, Y., Shamir, A., and Cohen-Or, D. Micro perceptual human computation for visual tasks. ACM Trans. Graph. 31, 5 (2012), 119:1--119:12. Google ScholarDigital Library
- Hartmann, B., Yu, L., Allison, A., Yang, Y., and Klemmer, S. R. Design as exploration: Creating interface alternatives through parallel authoring and runtime tuning. In Proc. UIST '08, ACM (2008), 91--100. Google ScholarDigital Library
- Kajino, H., Tsuboi, Y., and Kashima, H. Clustering crowds. In Proc. AAAI '13 (2013).Google Scholar
- Lee, B., Srivastava, S., Kumar, R., Brafman, R., and Klemmer, S. R. Designing with interactive example galleries. In Proc. CHI '10, ACM (2010), 2257--2266. Google ScholarDigital Library
- Lewis, J. P., Anjyo, K., Rhee, T., Zhang, M., Pighin, F., and Deng, Z. Practice and theory of blendshape facial models. In EG 2014 STARs, Eurographics Association (2014), 199--218.Google Scholar
- 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. Design galleries: A general approach to setting parameters for computer graphics and animation. In Proc. SIGGRAPH '97, ACM Press/Addison-Wesley Publishing Co. (1997), 389--400. Google ScholarDigital Library
- Ngan, A., Durand, F., and Matusik, W. Image-driven navigation of analytical brdf models. In Proc. EGSR '06, Eurographics Association (2006), 399--407. Google ScholarDigital Library
- Okabe, M., Matsushita, Y., Shen, L., and Igarashi, T. Illumination brush: Interactive design of all-frequency lighting. In Proc. Pacific Graphics '07 (2007), 171--180. Google ScholarDigital Library
- Prevost, R., Whiting, E., Lefebvre, S., and Sorkine-Hornung, O. Make it stand: Balancing shapes for 3d fabrication. ACM Trans. Graph. 32, 4 (2013), 81:1--81:10. Google ScholarDigital Library
- Quinn, A. J., and Bederson, B. B. Human computation: A survey and taxonomy of a growing field. In Proc. CHI '11, ACM (2011), 1403--1412. Google ScholarDigital Library
- Reinecke, K., and Gajos, K. Z. Quantifying visual preferences around the world. In Proc. CHI '14, ACM (2014), 11--20. Google ScholarDigital Library
- Reinecke, K., Yeh, T., Miratrix, L., Mardiko, R., Zhao, Y., Liu, J., and Gajos, K. Z. Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness. In Proc. CHI '13, ACM (2013), 2049--2058. Google ScholarDigital Library
- Sagar, M. Facial performance capture and expressive translation for king kong. In ACM SIGGRAPH 2006 Sketches, ACM (2006). Google ScholarDigital Library
- Secord, A., Lu, J., finkelstein, A., Singh, M., and Nealen, A. Perceptual models of viewpoint preference. ACM Trans. Graph. 30, 5 (2011), 109:1--109:12. Google ScholarDigital Library
- Shapira, L., Shamir, A., and Cohen-Or, D. Image appearance exploration by model-based navigation. Comput. Graph. Forum 28, 2 (2009), 629--638.Google ScholarCross Ref
- Sorkine, O. Differential representations for mesh processing. Comput. Graph. Forum 25, 4 (2006), 789--807.Google ScholarCross Ref
- Sykora, D., Sedlacek, D., Jinchao, S., Dingliana, J., and Collins, S. Adding depth to cartoons using sparse depth (in)equalities. Comput. Graph. Forum 29, 2 (2010), 615--623.Google ScholarCross Ref
- Talton, J. O., Gibson, D., Yang, L., Hanrahan, P., and Koltun, V. Exploratory modeling with collaborative design spaces. ACM Trans. Graph. 28, 5 (2009), 167:1--167:10. Google ScholarDigital Library
- Talton, J. O., Lou, Y., Lesser, S., Duke, J., Mech, R., and Koltun, V. Metropolis procedural modeling. ACM Trans. Graph. 30, 2 (Apr. 2011), 11:1--11:14. Google ScholarDigital Library
- Tamuz, O., Liu, C., Belongie, S., Shamir, O., and Kalai, A. Adaptively learning the crowd kernel. In Proc. ICML '11, ACM (2011), 673--680.Google Scholar
- Terry, M., and Mynatt, E. D. Side views: Persistent, on-demand previews for open-ended tasks. In Proc. UIST '02, ACM (2002), 71--80. Google ScholarDigital Library
- Terry, M., Mynatt, E. D., Nakakoji, K., and Yamamoto, Y. Variation in element and action: Supporting simultaneous development of alternative solutions. In Proc. CHI '04, ACM (2004), 711--718. Google ScholarDigital Library
- Willett, W., Heer, J., and Agrawala, M. Scented widgets: Improving navigation cues with embedded visualizations. IEEE Trans. Visual. Comput. Graphics 13, 6 (2007), 1129--1136. Google ScholarDigital Library
- Witkin, A., and Kass, M. Spacetime constraints. SIGGRAPH Comput. Graph. 22, 4 (1988), 159--168. Google ScholarDigital Library
- Xu, A., Huang, S.-W., and Bailey, B. Voyant: Generating structured feedback on visual designs using a crowd of non-experts. In Proc. CSCW '14, ACM (2014), 1433--1444. Google ScholarDigital Library
Index Terms
- Crowd-powered parameter analysis for visual design exploration
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