  The generation of well-designed artwork is often quite time-consuming and
assumes a high degree of proficiency on part of the human painter. In order to
facilitate the human painting process, substantial research efforts have been
made on teaching machines how to "paint like a human", and then using the
trained agent as a painting assistant tool for human users. However, current
research in this direction is often reliant on a progressive grid-based
division strategy wherein the agent divides the overall image into successively
finer grids, and then proceeds to paint each of them in parallel. This
inevitably leads to artificial painting sequences which are not easily
intelligible to human users. To address this, we propose a novel painting
approach which learns to generate output canvases while exhibiting a more
human-like painting style. The proposed painting pipeline Intelli-Paint
consists of 1) a progressive layering strategy which allows the agent to first
paint a natural background scene representation before adding in each of the
foreground objects in a progressive fashion. 2) We also introduce a novel
sequential brushstroke guidance strategy which helps the painting agent to
shift its attention between different image regions in a semantic-aware manner.
3) Finally, we propose a brushstroke regularization strategy which allows for
~60-80% reduction in the total number of required brushstrokes without any
perceivable differences in the quality of the generated canvases. Through both
quantitative and qualitative results, we show that the resulting agents not
only show enhanced efficiency in output canvas generation but also exhibit a
more natural-looking painting style which would better assist human users
express their ideas through digital artwork.
