We discuss the problem of computationally generating images resembling those of lost cultural patrimony, specifically two-dimensional artworks such as paintings and drawings. We view the problem as one of computing an estimate of the image in the lost work that best conforms to surviving information in a variety of forms: works by the source artist, including preparatory works such as cartoons for the target work; copies of the target by other artists; other works by these artists that reveal aspects of their style; historical knowledge of art methods. and materials; stylistic conventions of the relevant era; textual descriptions of the lost work and as well as more generally, images associated with stories given by the target’s title. Some of the general information linking images and text can be learned from large corpora of natural photographs and accompanying text scraped from the web. We present some preliminary, proof-of-concept simulations for recovering lost artworks with a special focus on textual information about target artworks. We outline our future directions, such as methods for assessing the contributions of different forms of information in the overall task of recovering lost artworks.
The automatic analysis of fine art paintings presents a number of novel technical challenges to artificial intelligence, computer vision, machine learning, and knowledge representation quite distinct from those arising in the analysis of traditional photographs. The most important difference is that many realist paintings depict stories or episodes in order to convey a lesson, moral, or meaning. One early step in automatic interpretation and extraction of meaning in artworks is the identifications of figures (“actors”). In Christian art, specifically, one must identify the actors in order to identify the Biblical episode or story depicted, an important step in “understanding” the artwork. We designed an auto-matic system based on deep convolutional neural net-works and simple knowledge database to identify saints throughout six centuries of Christian art based in large part upon saints’ symbols or attributes. Our work rep-resents initial steps in the broad task of automatic se- mantic interpretation of messages and meaning in fine art.
We apply generative adversarial convolutional neural networks to the problem of style transfer to underdrawings and ghost-images in x-rays of fine art paintings with a special focus on enhancing their spatial resolution. We build upon a neural architecture developed for the related problem of synthesizing high-resolution photo-realistic image from semantic label maps. Our neural architecture achieves high resolution through a hierarchy of generators and discriminator sub-networks, working throughout a range of spatial resolutions. This coarse-to-fine generator architecture can increase the effective resolution by a factor of eight in each spatial direction, or an overall increase in number of pixels by a factor of 64. We also show that even just a few examples of human-generated image segmentations can greatly improve—qualitatively and quantitatively—the generated images. We demonstrate our method on works such as Leonardo’s Madonna of the carnation and the underdrawing in his Virgin of the rocks, which pose several special problems in style transfer, including the paucity of representative works from which to learn and transfer style information.
We describe the application of convolutional neural network style transfer to the problem of improved visualization of underdrawings and ghost-paintings in fine art oil paintings. Such underdrawings and hidden paintings are typically revealed by x-ray or infrared techniques which yield images that are grayscale, and thus devoid of color and full style information. Past methods for inferring color in underdrawings have been based on physical x-ray uorescence spectral imaging of pigments in ghost-paintings and are thus expensive, time consuming, and require equipment not available in most conservation studios. Our algorithmic methods do not need such expensive physical imaging devices. Our proof-ofconcept system, applied to works by Pablo Picasso and Leonardo, reveal colors and designs that respect the natural segmentation in the ghost-painting. We believe the computed images provide insight into the artist and associated oeuvre not available by other means. Our results strongly suggest that future applications based on larger corpora of paintings for training will display color schemes and designs that even more closely resemble works of the artist. For these reasons refinements to our methods should find wide use in art conservation, connoisseurship, and art analysis.