As Picasso said, a painting lives only through the one who looks at it. To materialize this thought, we propose to automatically produce artworks that visually transform paintings by amplifying and distorting the most observed areas by viewers. Our work is based on a study conducted at the Caen Museum of Fine Arts in France. During the study, 151 participants were equipped with eye-tracking glasses, and observed various paintings, first alone and then in pairs. Based on the fixation and gaze path stored data, we first generate saliency maps that reflect the visual attention given to each painting. These maps are then used to fine-tune the UNETRSal model, a neural network designed to predict saliency maps, in order to align its outputs with human visual patterns observed during the experiment. The saliency maps generated are subsequently used to create deformations of the original painting. This overall process gives rise to a new artwork born from the interaction between human gaze and AI-prediction.