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Three Stage Drawing Transfer: Collaborative Drawing Between a Generative Adversarial Network, Co-robotic Arm, and Five-Year-Old Child

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Published:07 September 2022Publication History
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Abstract

This project creates a visual-mental-physical circuit between a Generative Adversarial Network (GAN), a co-robotic arm, and a five-year-old child. From training images to the latent space of a GAN, through pen on paper to a live human collaborator, it establishes a series of translational stages between humans and non-humans played out through the medium of drawing. Trained on a subset of the Rhoda Kellogg Child Art Collection, the neural network at the center of this piece learns its own representations of these images. The generated results, synthetic children's drawings, are of interest both for being outside of adult conventions and learned expression-like Dubuffet's art brut or Surrealist automatism-and for how they align machine learning with the human act of learning to draw.

The project layers many kinds of agency and embodiment: from the thousands of anonymous children who produced the original artwork used as training data, through the co-robot drawing from GAN-generated imagery, to the human child's active perception and graphic response to the robot. These questions of where we search for the other; when we attribute autonomy and intelligence; and why we might wish to escape our human subjectivities speak to core issues in the design and use of AI systems. This project is one attempt to think through those questions in an embodied way.

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References

  1. Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020. Analyzing and Improving the Image Quality of Stylegan. Proceedings of IEEE / CVF Computer Vision and Pattern Recognition. https://doi.org/10.48550/arXiv.1912.04958Google ScholarGoogle ScholarCross RefCross Ref
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  3. Rhoda Kellogg. n.d. Rhoda Kellogg Child Art Collection. Retrieved January 21, 2022 from https://www.early-pictures.ch/kellogg/archive/en/.Google ScholarGoogle Scholar
  4. Tzu-Mao Li. 2022. diffvg. Retrieved April 25, 2022 from https://github.com/BachiLi/diffvg.Google ScholarGoogle Scholar
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            cover image Proceedings of the ACM on Computer Graphics and Interactive Techniques
            Proceedings of the ACM on Computer Graphics and Interactive Techniques  Volume 5, Issue 4
            September 2022
            145 pages
            EISSN:2577-6193
            DOI:10.1145/3563103
            Issue’s Table of Contents

            Copyright © 2022 ACM

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            Publication History

            • Published: 7 September 2022
            Published in pacmcgit Volume 5, Issue 4

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