A Walk Through the Latent Space Using Computational Aesthetics as a Compass

A Walk Through the Latent Space Using Computational Aesthetics as a Compass

Sardenberg, Victor; Guatelli, Igor; Becker, Mirco

Full Article:

Generative Adversarial Network (GAN) models produce a latent space where many new images emerge. These models translate vectors from a latent space of possible designs into actual images, introducing a new degree of variability to the concept of objectile. This research proposes applying a computational aesthetics framework to navigate the latent space and present the designer with new images for feeding their imagination. Theories of parts to whole from aesthetics and cognitive psychology are combined with Birkhoff’s aesthetic measure and computer vision to predict aesthetic preferences and map the latent space.

Generative Adversarial Network (GAN) models produce a latent space where many new images emerge. These models translate vectors from a latent space of possible designs into actual images, introducing a new degree of variability to the concept of objectile. This research proposes applying a computational aesthetics framework to navigate the latent space and present the designer with new images for feeding their imagination. Theories of parts to whole from aesthetics and cognitive psychology are combined with Birkhoff’s aesthetic measure and computer vision to predict aesthetic preferences and map the latent space.

Palavras-chave: - -

DOI: 10.5151/sigradi2023-80

Referências bibliográficas

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Como citar:

Sardenberg, Victor; Guatelli, Igor; Becker, Mirco; "A Walk Through the Latent Space Using Computational Aesthetics as a Compass", p-1486-1497. In: . São Paulo: Blucher, 2024.
ISSN 23186968, DOI 10.5151/sigradi2023-80