  Video style transfer techniques inspire many exciting applications on mobile
devices. However, their efficiency and stability are still far from
satisfactory. To boost the transfer stability across frames, optical flow is
widely adopted, despite its high computational complexity, e.g. occupying over
97% inference time. This paper proposes to learn a lightweight video style
transfer network via knowledge distillation paradigm. We adopt two teacher
networks, one of which takes optical flow during inference while the other does
not. The output difference between these two teacher networks highlights the
improvements made by optical flow, which is then adopted to distill the target
student network. Furthermore, a low-rank distillation loss is employed to
stabilize the output of student network by mimicking the rank of input videos.
Extensive experiments demonstrate that our student network without an optical
flow module is still able to generate stable video and runs much faster than
the teacher network.
