  We propose a new approach to designing visual markers (analogous to QR-codes,
markers for augmented reality, and robotic fiducial tags) based on the advances
in deep generative networks. In our approach, the markers are obtained as color
images synthesized by a deep network from input bit strings, whereas another
deep network is trained to recover the bit strings back from the photos of
these markers. The two networks are trained simultaneously in a joint
backpropagation process that takes characteristic photometric and geometric
distortions associated with marker fabrication and marker scanning into
account. Additionally, a stylization loss based on statistics of activations in
a pretrained classification network can be inserted into the learning in order
to shift the marker appearance towards some texture prototype. In the
experiments, we demonstrate that the markers obtained using our approach are
capable of retaining bit strings that are long enough to be practical. The
ability to automatically adapt markers according to the usage scenario and the
desired capacity as well as the ability to combine information encoding with
artistic stylization are the unique properties of our approach. As a byproduct,
our approach provides an insight on the structure of patterns that are most
suitable for recognition by ConvNets and on their ability to distinguish
composite patterns.
