Flow-Guided Transformer for Video Inpainting

TL;DR

This paper introduces a flow-guided transformer that uses optical flow information to improve high-fidelity video inpainting by propagating content across frames and selectively guiding attention.

Contribution

It presents a novel flow completion network and a flow-guided transformer with flow reweighting and window partition strategies for efficient, high-quality video inpainting.

Findings

Effective in restoring corrupted video regions

Outperforms existing methods quantitatively and qualitatively

Efficient due to window partition and dual perspective attention

Abstract

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.