AnimateSVG: Autonomous Creation and Aesthetics Evaluation of Scalable Vector Graphics Animations for the Case of Brand Logos - Mateja, Deborah; Armbruster, Rebecca; Baumert, Jonathan; Bleil, Tim; Langenbahn, Jakob; Schwedhelm, Jan Christian; Sester, Sarah; Heinzl, Armin (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
In the light of the constant battle for attention on digital media, animating digital content plays an increasing role in modern graphic design. In this study, we use artificial intelligence methods to create aesthetic animations along the case of brand logos. With scalable vector graphics as the standard format in modern graphic design, we develop an autonomous end-to-end method using complex machine learning techniques to create brand logo animations as scalable vector graphics from scratch. We acquire data and setup a comprehensive animation space to create novel animations and evaluate them based on their aesthetics. We propose and compare two alternative computational models for automated logo animation and carefully weigh up their idiosyncrasies: on the one hand, we set up an aesthetics evaluation model to train an animation generator and, on the other hand, we combine tree ensembles with global optimization. Indeed, our proposed methods are capable of creating aesthetic logo animations, receiving an average rating of ‘good’ from observers.