  Controllable painting generation plays a pivotal role in image stylization.
Currently, the control way of style transfer is subject to exemplar-based
reference or a random one-hot vector guidance. Few works focus on decoupling
the intrinsic properties of painting as control conditions, e.g., artist, genre
and period. Under this circumstance, we propose a novel framework adopting
multiple attributes from the painting to control the stylized results. An
asymmetrical cycle structure is equipped to preserve the fidelity, associating
with style preserving and attribute regression loss to keep the unique
distinction of colors and textures between domains. Several qualitative and
quantitative results demonstrate the effect of the combinations of multiple
attributes and achieve satisfactory performance.
