TL;DR
This paper introduces a mask-aware inpainting framework that dynamically adapts to missing regions using specialized filtering and normalization, significantly improving the quality of inpainted images.
Contribution
The novel mask-aware inpainting model employs dynamic filtering and point-wise normalization to better handle arbitrary missing regions in an end-to-end cascaded refinement architecture.
Findings
Outperforms existing methods on Places2, CelebA, and Paris StreetView datasets.
Effectively captures multi-scale features for missing regions.
Produces visually coherent inpainted images with higher quantitative scores.
Abstract
Inpainting arbitrary missing regions is challenging because learning valid features for various masked regions is nontrivial. Though U-shaped encoder-decoder frameworks have been witnessed to be successful, most of them share a common drawback of mask unawareness in feature extraction because all convolution windows (or regions), including those with various shapes of missing pixels, are treated equally and filtered with fixed learned kernels. To this end, we propose our novel mask-aware inpainting solution. Firstly, a Mask-Aware Dynamic Filtering (MADF) module is designed to effectively learn multi-scale features for missing regions in the encoding phase. Specifically, filters for each convolution window are generated from features of the corresponding region of the mask. The second fold of mask awareness is achieved by adopting Point-wise Normalization (PN) in our decoding phase,…
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Taxonomy
MethodsInpainting · Convolution
