Transforming Visual Data into Art: Evaluating AI's Capacity to Replicate Artistic Styles
Transforming Visual Data into Art: Evaluating AI's Capacity to Replicate Artistic Styles

Summary/Abstract: Training artificial intelligence applications by uploading visuals is a form of converting visual data into another. Subsequently, generating visuals by prompting with trained artificial intelligence is an operation of transforming previously converted data back into visuals. Through such applications, an artist's works can be replicated, amalgamated with different art movements, or entirely novel works can be produced as if crafted by the same artist. However, how successful are applications like Stable Diffusion or Leonardo in this process? To ascertain this, various artificial intelligence applications will be trained with a painter's works, and the resulting outputs will be evaluated in consultation with the artists to assess the efficacy of contemporary AI applications in this domain. To assess the suitability of the images in the mentioned project, several factors will be considered, such as: resolution and clarity, variety of subjects, quality of lighting, composition, color accuracy, diversity in artistic styles, image metadata.

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