Texture Transform Attention for Realistic Image Inpainting

• URL: http://arxiv.org/abs/2012.04242v1
• Date: Tue, 8 Dec 2020 06:28:51 GMT
• Title: Texture Transform Attention for Realistic Image Inpainting
• Authors: Yejin Kim and Manri Cheon and Junwoo Lee
• Abstract summary: We propose a Texture Transform Attention network that better produces the missing region inpainting with fine details. Texture Transform Attention is used to create a new reassembled texture map using fine textures and coarse semantics. We evaluate our model end-to-end with the publicly available datasets CelebA-HQ and Places2.
• Score: 6.275013056564918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the last few years, the performance of inpainting to fill missing regions has shown significant improvements by using deep neural networks. Most of inpainting work create a visually plausible structure and texture, however, due to them often generating a blurry result, final outcomes appear unrealistic and make feel heterogeneity. In order to solve this problem, the existing methods have used a patch based solution with deep neural network, however, these methods also cannot transfer the texture properly. Motivated by these observation, we propose a patch based method. Texture Transform Attention network(TTA-Net) that better produces the missing region inpainting with fine details. The task is a single refinement network and takes the form of U-Net architecture that transfers fine texture features of encoder to coarse semantic features of decoder through skip-connection. Texture Transform Attention is used to create a new reassembled texture map using fine textures and coarse semantics that can efficiently transfer texture information as a result. To stabilize training process, we use a VGG feature layer of ground truth and patch discriminator. We evaluate our model end-to-end with the publicly available datasets CelebA-HQ and Places2 and demonstrate that images of higher quality can be obtained to the existing state-of-the-art approaches.

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