Search Results for author: Camilo Cruz Gambardella

Found 8 papers, 2 papers with code

Taming Stable Diffusion for Text to 360° Panorama Image Generation

2 code implementations11 Apr 2024 Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xiaoshui Huang, Dinh Phung, Wanli Ouyang, Jianfei Cai

Generative models, e. g., Stable Diffusion, have enabled the creation of photorealistic images from text prompts.

Denoising Image Generation

Creative Discovery using QD Search

1 code implementation8 May 2023 Jon McCormack, Camilo Cruz Gambardella, Stephen James Krol

In creative design, where aesthetics play a crucial role in determining the quality of outcomes, there are often multiple worthwhile possibilities, rather than a single ``best'' design.

Image Classification

Quality-diversity for aesthetic evolution

no code implementations4 Feb 2022 Jon McCormack, Camilo Cruz Gambardella

We show that the quality-diversity search is able to find multiple phenotypes of high aesthetic value.

Complexity and Aesthetics in Generative and Evolutionary Art

no code implementations5 Jan 2022 Jon McCormack, Camilo Cruz Gambardella

We apply a series of different complexity measures to three different evolutionary art datasets and look at the correlations between complexity and individual aesthetic judgement by the artist (in the case of two datasets) or the physically measured complexity of generative 3D forms.

Growing and Evolving 3D Prints

no code implementations7 Jul 2021 Jon McCormack, Camilo Cruz Gambardella

We find that by evolving first for aesthetic complexity, then evolving for structural consistency until the form is 'just printable', gives the best results.

Searching for Designs in-between

no code implementations11 Feb 2021 Camilo Cruz Gambardella, Jon McCormack

The use of evolutionary methods in design and art is increasing in diversity and popularity.

The Enigma of Complexity

no code implementations3 Feb 2021 Jon McCormack, Camilo Cruz Gambardella, Andy Lomas

We apply a series of different complexity measures to three different generative art datasets and look at the correlations between complexity and individual aesthetic judgement by the artist (in the case of two datasets) or the physically measured complexity of 3D forms.

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