Creative Generation of 3D Objects with Deep Learning and Innovation Engines

Joel Anthony Lehman, Sebastian Risi, Jeff Clune

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


Advances in supervised learning with deep neural networks have enabled robust classification in many real world domains. An interesting question is if such advances can also be leveraged effectively for computational creativity. One insight is that because evolutionary algorithms are free from strict requirements of mathematical smoothness, they can exploit powerful deep learning representations through arbitrary computational pipelines. In this way, deep networks trained on typical supervised tasks can be used as an ingredient in an evolutionary algorithm driven towards creativity. To highlight such potential, this paper creates novel 3D objects by leveraging feedback from a deep network trained only to recognize 2D images. This idea is tested
by extending previous work with Innovation Engines, i.e. a principled combination of deep learning and evolutionary algorithms for computational creativity. The results of this automated process are interesting and recognizable 3D-printable objects, demonstrating the creative potential for combining evolutionary computation and deep learning in this way.
Original languageEnglish
Title of host publicationProceedings of the Seventh International Conference on Computational Creativity : ICCC 2016
PublisherSony CSL Paris
Publication date30 Jun 2016
ISBN (Print)9782746691551
ISBN (Electronic)9782746691551
Publication statusPublished - 30 Jun 2016
EventInternational Conference on Computational Creativity - UPMC, Paris, France
Duration: 27 Jun 20161 Jul 2016
Conference number: 7


ConferenceInternational Conference on Computational Creativity
Internet address


  • Supervised Learning
  • Deep Neural Networks
  • Computational Creativity
  • Evolutionary Algorithms
  • 3D Object Generation


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