  Significant progress has been made with artistic robots. However, existing
robots fail to produce high-quality portraits in a short time. In this work, we
present a drawing robot, which can automatically transfer a facial picture to a
vivid portrait, and then draw it on paper within two minutes averagely. At the
heart of our system is a novel portrait synthesis algorithm based on deep
learning. Innovatively, we employ a self-consistency loss, which makes the
algorithm capable of generating continuous and smooth brush-strokes. Besides,
we propose a componential sparsity constraint to reduce the number of
brush-strokes over insignificant areas. We also implement a local sketch
synthesis algorithm, and several pre- and post-processing techniques to deal
with the background and details. The portrait produced by our algorithm
successfully captures individual characteristics by using a sparse set of
continuous brush-strokes. Finally, the portrait is converted to a sequence of
trajectories and reproduced by a 3-degree-of-freedom robotic arm. The whole
portrait drawing robotic system is named AiSketcher. Extensive experiments show
that AiSketcher can produce considerably high-quality sketches for a wide range
of pictures, including faces in-the-wild and universal images of arbitrary
content. To our best knowledge, AiSketcher is the first portrait drawing robot
that uses neural style transfer techniques. AiSketcher has attended a quite
number of exhibitions and shown remarkable performance under diverse
circumstances.
