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.

本文提出了一种通过知识蒸馏范式学习轻量级视频风格转移到提高稳定性的方法，并采用两个教师网络，一个使用光流，另一个不使用，通过它们的输出差异，采用蒸馏方法训练目标学生网络，同时采用低秩蒸馏损失来稳定学生网络的输出，实验证明我们的学生网络不需要光流模块即可生成稳定视频，并比教师网络运行速度更快。