  Video inpainting aims to fill the given spatiotemporal holes with realistic
appearance but is still a challenging task even with prosperous deep learning
approaches. Recent works introduce the promising Transformer architecture into
deep video inpainting and achieve better performance. However, it still suffers
from synthesizing blurry texture as well as huge computational cost. Towards
this end, we propose a novel Decoupled Spatial-Temporal Transformer (DSTT) for
improving video inpainting with exceptional efficiency. Our proposed DSTT
disentangles the task of learning spatial-temporal attention into 2 sub-tasks:
one is for attending temporal object movements on different frames at same
spatial locations, which is achieved by temporally-decoupled Transformer block,
and the other is for attending similar background textures on same frame of all
spatial positions, which is achieved by spatially-decoupled Transformer block.
The interweaving stack of such two blocks makes our proposed model attend
background textures and moving objects more precisely, and thus the attended
plausible and temporally-coherent appearance can be propagated to fill the
holes. In addition, a hierarchical encoder is adopted before the stack of
Transformer blocks, for learning robust and hierarchical features that maintain
multi-level local spatial structure, resulting in the more representative token
vectors. Seamless combination of these two novel designs forms a better
spatial-temporal attention scheme and our proposed model achieves better
performance than state-of-the-art video inpainting approaches with significant
boosted efficiency.
