  This paper proposes an adaptive compact attention model for few-shot
video-to-video translation. Existing works in this domain only use features
from pixel-wise attention without considering the correlations among multiple
reference images, which leads to heavy computation but limited performance.
Therefore, we introduce a novel adaptive compact attention mechanism to
efficiently extract contextual features jointly from multiple reference images,
of which encoded view-dependent and motion-dependent information can
significantly benefit the synthesis of realistic videos. Our core idea is to
extract compact basis sets from all the reference images as higher-level
representations. To further improve the reliability, in the inference phase, we
also propose a novel method based on the Delaunay Triangulation algorithm to
automatically select the resourceful references according to the input label.
We extensively evaluate our method on a large-scale talking-head video dataset
and a human dancing dataset; the experimental results show the superior
performance of our method for producing photorealistic and temporally
consistent videos, and considerable improvements over the state-of-the-art
method.
