  We propose a flow-guided transformer, which innovatively leverage the motion
discrepancy exposed by optical flows to instruct the attention retrieval in
transformer for high fidelity video inpainting. More specially, we design a
novel flow completion network to complete the corrupted flows by exploiting the
relevant flow features in a local temporal window. With the completed flows, we
propagate the content across video frames, and adopt the flow-guided
transformer to synthesize the rest corrupted regions. We decouple transformers
along temporal and spatial dimension, so that we can easily integrate the
locally relevant completed flows to instruct spatial attention only.
Furthermore, we design a flow-reweight module to precisely control the impact
of completed flows on each spatial transformer. For the sake of efficiency, we
introduce window partition strategy to both spatial and temporal transformers.
Especially in spatial transformer, we design a dual perspective spatial MHSA,
which integrates the global tokens to the window-based attention. Extensive
experiments demonstrate the effectiveness of the proposed method qualitatively
and quantitatively. Codes are available at https://github.com/hitachinsk/FGT.
