Context-Preserving Two-Stage Video Domain Translation for Portrait
  Stylization
Doyeon Kim Eunji Ko Hyunsu Kim Yunji Kim Junho Kim Dongchan Min Junmo Kim Sung Ju Hwang
Portrait stylization, which translates a real human face image into an
artistically stylized image, has attracted considerable interest and many prior
works have shown impressive quality in recent years. However, despite their
remarkable performances in the image-level translation tasks, prior methods
show unsatisfactory results when they are applied to the video domain. To
address the issue, we propose a novel two-stage video translation framework
with an objective function which enforces a model to generate a temporally
coherent stylized video while preserving context in the source video.
Furthermore, our model runs in real-time with the latency of 0.011 seconds per
frame and requires only 5.6M parameters, and thus is widely applicable to
practical real-world applications.