  High-quality video inpainting that completes missing regions in video frames
is a promising yet challenging task. State-of-the-art approaches adopt
attention models to complete a frame by searching missing contents from
reference frames, and further complete whole videos frame by frame. However,
these approaches can suffer from inconsistent attention results along spatial
and temporal dimensions, which often leads to blurriness and temporal artifacts
in videos. In this paper, we propose to learn a joint Spatial-Temporal
Transformer Network (STTN) for video inpainting. Specifically, we
simultaneously fill missing regions in all input frames by self-attention, and
propose to optimize STTN by a spatial-temporal adversarial loss. To show the
superiority of the proposed model, we conduct both quantitative and qualitative
evaluations by using standard stationary masks and more realistic moving object
masks. Demo videos are available at https://github.com/researchmm/STTN.
