  This paper proposes a novel video inpainting method. We make three main
contributions: First, we extended previous Transformers with patch alignment by
introducing Deformed Patch-based Homography (DePtH), which improves patch-level
feature alignments without additional supervision and benefits challenging
scenes with various deformation. Second, we introduce Mask Pruning-based Patch
Attention (MPPA) to improve patch-wised feature matching by pruning out less
essential features and using saliency map. MPPA enhances matching accuracy
between warped tokens with invalid pixels. Third, we introduce a
Spatial-Temporal weighting Adaptor (STA) module to obtain accurate attention to
spatial-temporal tokens under the guidance of the Deformation Factor learned
from DePtH, especially for videos with agile motions. Experimental results
demonstrate that our method outperforms recent methods qualitatively and
quantitatively and achieves a new state-of-the-art.
