PhD Thesis

Generative Design of Novel Faces using  an Evolutionarily Driven Variable Focus Model

Senior Supervisor: Steve DiPaola
Supervisor: Jim Bizzocchi

My PhD research focuses on modeling variable focus in creativity support tool interfaces that use evolutionary algorithms with interactive/aesthetic selection for generative purposes. My aim is to explore the potential designs of generative creativity support tools to enhance the user’s creativity in human-computer co-creativity scenarios.

A key component of creativity has been argued to be variable focus, the ability to switch between two modes of thinking: conceptual fluidity and focus (Gabora, 2002). Interactive Evolutionary Algorithms can be employed to reflect both modes of thinking. While the computer generates variations of a prototypical design through lateral exploration of the solution space through evolutionary approaches, the user is responsible for selecting from the generated designs and when needed manually editing individual designs, traversing the space vertically, fine tuning features and configurations of a design, mimicking the analytical phase of the variable focus model. Through alternating between a convergent/analytical/focus phase and a divergent/fluid/associative/lateral ones, I hypothesize that the user will avoid being stuck at local optimas and will be able to find novel alternatives to their current solution, resulting in artifacts and solutions that may be perceived as more creative. This approach places the computer in a co-creator role in a mixed-initiative interaction scenario.

Overview of proposed workflow

Initially, a random seed is applied to a certain number of solutions and the resulting designs are visualized in a grid of designs. The user is able to choose certain designs and breed them with each other, generating new populations that evolve towards the user’s assumed choices, using the features from their selections. This phase it the computer providing the user with various potential solutions that are similar to the user’s solutions and their different combinations that are revealed through the evolutionary approach.

At any point, the user can decide to explore a design vertically by isolating it and editing its individual features. This phase it mirrors the analytical, focused phase of the variable focus model. The user can edit, lock, save certain designs, keeping them exempt from the pool of designs that will be discarded or evolved and may also choose to introduce them into the pool at a later time.

In essence, the computer generates new variations, levaraging the technology to quickly configure and visualize designs while the user assumes to role of the evaluator, judging which directions the computer should generate further designs in. Combined with the ability to focus and fine-tune each design, this workflow reflects a model of human creativity that may lead to more novel, creative solutions.

In this thesis, I focused on creating human face designs, but any design object that can be parameterized could potentially be plugged in to the system.

Above figure illustratos how a face’s nose length is evolved from the user’s selections. The diagram shows a number of values for given parameters that describe facial features such as nose length and lip fullness. In the evolution process, faces with various different features are selected by the user. These selected faces are then bred among each other for another round of iteration, optimizing towards a desired aesthetic goal through the optimization of multiple parameters.

The following screens are from early UX sketches that show some of the screens and features.

contextual rmb menu on a selected item
editing an item’s individual parameters w/sliders
mutation intensity setting and tooltip example

Captures from the w.i.p. application

  • Create random seed to generate initial designs
  • Choose designs to evolve features from
  • Evolve new populatiAons from selected faces
  • Apply mutations/random deviations
  • Isolate and focus on a single solution for fine tuning
  • Fine tune a design focusing on individual features
  • Breed, randomize or further fine tune a design until design goals are met

Bibliography (so far)

Creative Evolutionary Systems

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  1. DiPaola, Steve, and Nathan Sorenson. “CGP, Creativity and Art.” Cartesian Genetic Programming, Springer, Berlin, Heidelberg, 2011, pp. 293–307. link.springer.com, doi:10.1007/978-3-642-17310-3_10.
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Creative Cognition

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  1. Cosmelli, Diego, and David D. Preiss. “On the Temporality of Creative Insight: A Psychological and Phenomenological Perspective.” Frontiers in Psychology, vol. 5, 2014. Frontiers, doi:10.3389/fpsyg.2014.01184.
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Creativity Support Tools

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  1. Shneiderman, B. Creativity support tools: A grand challenge for HCI researchers. In Miguel Redondo, Crescencio Bravo, and Manuel Ortega, editors, Engineering The User Interface, pages 1–9. Springer-Verlag London Limited, 2009
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  1. Antonios Liapis and Georgios N. Yannakakis. Boosting computational creativity with human interaction in mixed-initiative co-creation tasks. In Proceedings of the ICCC workshop on Computational Creativity and Games, 2016
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  1. Kantosalo, Anna, and Hannu Toivonen. “Modes for creative human-computer collaboration: Alternating and task-divided co-creativity.” Proceedings of the Seventh International Conference on Computational Creativity. 2016.
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  1. Bown, O. 2014. Empirically grounding the evaluation of creative systems: Incorporating interaction design. In Proceedings of the Fifth International Conference on Computational Creativity, 112–119.
  1. Kantosalo, A.; Toivanen, J. M.; and Toivonen, H. 2015. Interaction evaluation for human-computer co-creativity: A case study.  In Proceedings of the International Conference on Computational Creativity.
  1. Compton, K., and Mateas, M. 2015. Casual creators. In Proceedings of the International Conference on Computational Creativity.
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  1. Eales, R. T. “Creativity in action: some implications for the design of creativity support systems.” Proceedings of the 6th ACM SIGCHI New Zealand chapter’s international conference on Computer-human interaction: making CHI natural. ACM, 2005.
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  1. Cherry, Erin, and Celine Latulipe. “Quantifying the Creativity Support of Digital Tools Through the Creativity Support Index.” ACM Trans. Comput.-Hum. Interact., vol. 21, no. 4, June 2014, pp. 21:1–21:25. ACM Digital Library, doi:10.1145/2617588.

3D Graphics

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  1. Chaudhuri, Siddhartha, and Vladlen Koltun. “Data-Driven Suggestions for Creativity Support in 3D Modeling.” ACM SIGGRAPH Asia 2010 Papers, ACM, 2010, pp. 183:1–183:10. ACM Digital Library, doi:10.1145/1866158.1866205.
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  1. Kazi, Rubaiat Habib, et al. “DreamSketch: Early Stage 3D Design Explorations with Sketching and Generative Design.” UIST. 2017.
  1. Andre, Alexis, and Suguru Saito. “Single-view sketch based modeling.” Proceedings of the Eighth Eurographics Symposium on Sketch-Based Interfaces and Modeling. ACM, 2011.
  1. Lun, Zhaoliang, Evangelos Kalogerakis, and Alla Sheffer. “Elements of style: learning perceptual shape style similarity.” ACM Transactions on Graphics (TOG) 34.4 (2015): 84.
  1. Ma, Chongyang, et al. “Analogy‐driven 3D style transfer.” Computer Graphics Forum. Vol. 33. No. 2. 2014.
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  1. Torres, Cesar, and Eric Paulos. “MetaMorphe: Designing expressive 3D models for digital fabrication.” Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition. ACM, 2015.
  1. Talton, Jerry O., et al. “Exploratory Modeling with Collaborative Design Spaces.” ACM SIGGRAPH Asia 2009 Papers, ACM, 2009, pp. 167:1–167:10. ACM Digital Library, doi:10.1145/1661412.1618513.
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  1. Xu, Kai, et al. “Fit and Diverse: Set Evolution for Inspiring 3D Shape Galleries.” ACM Trans. Graph., vol. 31, no. 4, July 2012, pp. 57:1–57:10. ACM Digital Library, doi:10.1145/2185520.2185553.
  1. Kim, Vladimir G., et al. “Exploring Collections of 3D Models Using Fuzzy Correspondences.” ACM Trans. Graph., vol. 31, no. 4, July 2012, pp. 54:1–54:11. ACM Digital Library, doi:10.1145/2185520.2185550.
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Face Perception

  1. Burleigh, Tyler J., et al. “Does the Uncanny Valley Exist? An Empirical Test of the Relationship between Eeriness and the Human Likeness of Digitally Created Faces.” Computers in Human Behavior, vol. 29, no. 3, May 2013, pp. 759–71. ScienceDirect, doi:10.1016/j.chb.2012.11.021.
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  1. Dill, Vanderson, et al. “Evaluation of the Uncanny Valley in CG Characters.” Intelligent Virtual Agents, edited by Yukiko Nakano et al., Springer Berlin Heidelberg, 2012, pp. 511–13.
  1. Ferstl, Ylva, and Rachel McDonnell. “A Perceptual Study on the Manipulation of Facial Features for Trait Portrayal in Virtual Agents.” Proceedings of the 18th International Conference on Intelligent Virtual Agents, ACM, 2018, pp. 281–288. ACM Digital Library, doi:10.1145/3267851.3267891.
  1. Gold, Jason M., et al. “The Perception of a Face Is No More Than the Sum of Its Parts.” Psychological Science, vol. 23, no. 4, Apr. 2012, pp. 427–34. SAGE Journals, doi:10.1177/0956797611427407.
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  1. McDonnell, Rachel, et al. “Render Me Real?: Investigating the Effect of Render Style on the Perception of Animated Virtual Humans.” ACM Trans. Graph., vol. 31, no. 4, July 2012, pp. 91:1–91:11. ACM Digital Library, doi:10.1145/2185520.2185587.
  1. MacDorman, Karl F., and Hiroshi Ishiguro. “The Uncanny Advantage of Using Androids in Cognitive and Social Science Research.” Interaction Studies, vol. 7, no. 3, Jan. 2006, pp. 297–337. www.jbe-platform.com, doi:10.1075/is.7.3.03mac.
  1. Mustafa, Maryam, and Marcus Magnor. “EEG Based Analysis of the Perception of Computer-Generated Faces.” Proceedings of the 13th European Conference on Visual Media Production (CVMP 2016), ACM, 2016, pp. 4:1–4:10. ACM Digital Library, doi:10.1145/2998559.2998563.
  1. Rakover, Sam S. “Featural vs. Configurational Information in Faces: A Conceptual and Empirical Analysis.” British Journal of Psychology, vol. 93, no. 1, Feb. 2002, pp. 1–30. onlinelibrary.wiley.com, doi:10.1348/000712602162427.
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  1. Zell, Eduard, and Mario Botsch. “ElastiFace: Matching and Blending Textured Faces.” Proceedings of the Symposium on Non-Photorealistic Animation and Rendering, ACM, 2013, pp. 15–24. ACM Digital Library, doi:10.1145/2486042.2486045.
  1. Zell, Eduard, et al. “To Stylize or Not to Stylize?: The Effect of Shape and Material Stylization on the Perception of Computer-Generated Faces.” ACM Trans. Graph., vol. 34, no. 6, Oct. 2015, pp. 184:1–184:12. ACM Digital Library, doi:10.1145/2816795.2818126.
  1. DiPaola, Steve. “Exploring a Parameterized Portrait Painting Space”,  International Journal of Art and Technology, Vol 2, No 1-2, pp 82-93, 2009  summit.sfu.ca, http://summit.sfu.ca/item/56.
  1. Anderson, Nicole D., and Hugh R. Wilson. “The Nature of Synthetic Face Adaptation.” Vision Research, vol. 45, no. 14, June 2005, pp. 1815–28. ScienceDirect, doi:10.1016/j.visres.2005.01.012.
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  1. Webster, Michael A., and Otto H. Maclin. “Figural After effects in the Perception of Faces.” Psychonomic Bulletin & Review, vol. 6, no. 4, Dec. 1999, pp. 647–53. Springer Link, doi:10.3758/BF03212974.
  1. Leopold, David A., et al. “Prototype-Referenced Shape Encoding Revealed by High-Level Aftereffects.” Nature Neuroscience, vol. 4, no. 1, Jan. 2001, pp. 89–94. www.nature.com, doi:10.1038/82947.
  1. Stirrat, M., and D. I. Perrett. “Valid Facial Cues to Cooperation and Trust: Male Facial Width and Trustworthiness.” Psychological Science, vol. 21, no. 3, Mar. 2010, pp. 349–54. SAGE Journals, doi:10.1177/0956797610362647.
  1. Oosterhof, Nikolaas N., and Alexander Todorov. “The Functional Basis of Face Evaluation.” Proceedings of the National Academy of Sciences, vol. 105, no. 32, Aug. 2008, pp. 11087–92. www.pnas.org, doi:10.1073/pnas.0805664105.