Video-to-video translation aims to generate video frames of a target domain
from an input video. Despite its usefulness, the existing networks require
enormous computations, necessitating their model compression for wide use.
While there exist compression methods that improve computational efficiency in
various image/video tasks, a generally-applicable compression method for
video-to-video translation has not been studied much. In response, we present
Shortcut-V2V, a general-purpose compression framework for video-to-video
translation. Shourcut-V2V avoids full inference for every neighboring video
frame by approximating the intermediate features of a current frame from those
of the previous frame. Moreover, in our framework, a newly-proposed block
called AdaBD adaptively blends and deforms features of neighboring frames,
which makes more accurate predictions of the intermediate features possible. We
conduct quantitative and qualitative evaluations using well-known
video-to-video translation models on various tasks to demonstrate the general
applicability of our framework. The results show that Shourcut-V2V achieves
comparable performance compared to the original video-to-video translation
model while saving 3.2-5.7x computational cost and 7.8-44x memory at test time.

Shortcut-V2V 是一个适用于视频到视频翻译的通用压缩框架，通过近似当前帧的中间特征，避免对每个相邻视频帧进行完整推理，利用 AdaBD 模块适应性地融合和变形相邻帧的特征，以实现更准确的中间特征预测，除能达到原始模型相当的性能外，还能在测试时节省 3.2-5.7 倍的计算成本和 7.8-44 倍的内存。