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Video inpainting aims to fill missing regions with plausible content in a video sequence. Deep learning-based video inpainting methods have made promising progress over the past few years. However, these methods tend to generate degraded completion content, such as missing textural details. To address this issue, we propose a novel Deformable Alignment and Pyramid-context Completion Network for video inpainting (DAPC-Net), which takes advantage of temporal redundancy information among video sequence. Specifically, we construct a deformable convolution alignment network (DANet) for aligning reference frame at the feature level. After alignment, we further devise a pyramid-context completion network (PCNet) to complete missing regions of the target frame. Particularly, the pyramid completion mechanism and cross-scale transference strategy are used to ensure the visual and semantic coherence of the completed target frame. Experimental results show that the proposed method not only achieves better quantitative and qualitative performance but also improves the inference speed by 35.4
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