mlsanityData to 14 Oct 2026

Human Machine Co-Creation. A Complementary Cognitive Approach to Creative Character Design Process Using GANs

arXiv 2311.13960 ↗First posted 23 Nov 2023cs.AIcited by 1

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Abstract (arXiv)

Recent advances in Generative Adversarial Networks GANs applications continue to attract the attention of researchers in different fields. In such a framework, two neural networks compete adversely to generate new visual contents indistinguishable from the original dataset. The objective of this research is to create a complementary codesign process between humans and machines to augment character designers abilities in visualizing and creating new characters for multimedia projects such as games and animation. Driven by design cognitive scaffolding, the proposed approach aims to inform the process of perceiving, knowing, and making. The machine generated concepts are used as a launching platform for character designers to conceptualize new characters. A labelled dataset of 22,000 characters was developed for this work and deployed using different GANs to evaluate the most suited for the context, followed by mixed methods evaluation for the machine output and human derivations. The discussed results substantiate the value of the proposed cocreation framework and elucidate how the generated concepts are used as cognitive substances that interact with designers competencies in a versatile manner to influence the creative processes of conceptualizing novel characters.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 30 of its 78 references that are in the map. First posted on arXiv 23 Nov 2023.

Builds on

30 of 78 references.

References · 30

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  6. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  7. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  8. 2017Improved Training of Wasserstein GANscited by 2,235
  9. 2014Conditional Generative Adversarial Netscited by 2,162
  10. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  11. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  12. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,355
18 more
  1. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,299
  2. 2020Training Generative Adversarial Networks with Limited Datacited by 1,186
  3. 2021Alias-Free Generative Adversarial Networkscited by 1,184
  4. 2018Self-Attention Generative Adversarial Networkscited by 809
  5. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  6. 2017Generative Adversarial Networks: An Overviewcited by 590
  7. 2016A Learned Representation For Artistic Stylecited by 341
  8. 2015A note on the evaluation of generative modelscited by 292
  9. 2019Large Scale Adversarial Representation Learningcited by 239
  10. 2018Pros and Cons of GAN Evaluation Measurescited by 224
  11. 2017Modulating early visual processing by languagecited by 167
  12. 2016Learning What and Where to Drawcited by 105
  13. 2022Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systemscited by 59
  14. 2018Improving Shape Deformation in Unsupervised Image-to-Image Translationcited by 19
  15. 2019Sparse Generative Adversarial Networkcited by 8
  16. 2020Five Points to Check when Comparing Visual Perception in Humans and Machinescited by 8
  17. 2018GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraintcited by 6
  18. 2021Human-machine Symbiosis: A Multivariate Perspective for Physically Coupled Human-machine Systemscited by 4

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