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Authors: Jireh Jam 1 ; Connah Kendrick 1 ; Vincent Drouard 2 ; Kevin Walker 2 ; Gee-Sern Hsu 3 and Moi Hoon Yap 1

Affiliations: 1 Manchester Metropolitan University, Manchester, U.K ; 2 Image Metrics Ltd, Manchester, U.K ; 3 National Taiwan University of Science&Technology, Taipei, Taiwan

Keyword(s): Inpainting, Generative Neural Networks, Hallucinations, Realism.

Abstract: The state-of-the-art facial image inpainting methods achieved promising results but face realism preservation remains a challenge. This is due to limitations such as; failures in preserving edges and blurry artefacts. To overcome these limitations, we propose a Symmetric Skip Connection Wasserstein Generative Adversarial Network (S-WGAN) for high-resolution facial image inpainting. The architecture is an encoder-decoder with convolutional blocks, linked by skip connections. The encoder is a feature extractor that captures data abstractions of an input image to learn an end-to-end mapping from an input (binary masked image) to the ground-truth. The decoder uses learned abstractions to reconstruct the image. With skip connections, S-WGAN transfers image details to the decoder. Additionally, we propose a Wasserstein-Perceptual loss function to preserve colour and maintain realism on a reconstructed image. We evaluate our method and the state-of-the-art methods on CelebA-HQ dataset. Our results show S-WGAN produces sharper and more realistic images when visually compared with other methods. The quantitative measures show our proposed S-WGAN achieves the best Structure Similarity Index Measure (SSIM) of 0.94. (More)

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Paper citation in several formats:
Jam, J.; Kendrick, C.; Drouard, V.; Walker, K.; Hsu, G. and Yap, M. (2021). Symmetric Skip Connection Wasserstein GAN for High-resolution Facial Image Inpainting. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 35-44. DOI: 10.5220/0010188700350044

@conference{visapp21,
author={Jireh Jam. and Connah Kendrick. and Vincent Drouard. and Kevin Walker. and Gee{-}Sern Hsu. and Moi Hoon Yap.},
title={Symmetric Skip Connection Wasserstein GAN for High-resolution Facial Image Inpainting},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={35-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010188700350044},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - Symmetric Skip Connection Wasserstein GAN for High-resolution Facial Image Inpainting
SN - 978-989-758-488-6
IS - 2184-4321
AU - Jam, J.
AU - Kendrick, C.
AU - Drouard, V.
AU - Walker, K.
AU - Hsu, G.
AU - Yap, M.
PY - 2021
SP - 35
EP - 44
DO - 10.5220/0010188700350044
PB - SciTePress

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