Our work on generative modelling of artistically painted eyes got accepted at MMM 2020 in Daejon, South Korea.
Faces in artistic paintings most often contain the same elements (eyes, nose, mouth…) as faces in the real world, however they are not a photo-realistic transfer of physical visual content. These creative nuances the artists introduce in their work act as interference when facial detection models are used in the artistic domain. In this work we introduce models that can accurately detect, classify and conditionally generate artistically painted eyes in portrait paintings. In addition, we introduce the OmniEyes Dataset that captures the essence of painted eyes with annotated patches from 250 K artistic paintings and their metadata. We evaluate our approach in inpainting, out of context eye generation and classification on portrait paintings from the OmniArt dataset. We conduct a user case study to further study the quality of our generated samples, asses their aesthetic aspects and provide quantitative and qualitative results
