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The state-of-the-art facial image inpainting methods achieved promisingresults but face realism preservation remains a challenge. This is due tolimitations such as; failures in preserving edges and blurry artefacts. Toovercome these limitations, we propose a Symmetric Skip Connection WassersteinGenerative Adversarial Network (S-WGAN) for high-resolution facial imageinpainting. The architecture is an encoder-decoder with convolutional blocks,linked by skip connections. The encoder is a feature extractor that capturesdata abstractions of an input image to learn an end-to-end mapping from aninput (binary masked image) to the ground-truth. The decoder uses learnedabstractions to reconstruct the image. With skip connections, S-WGAN transfersimage details to the decoder. Additionally, we propose a Wasserstein-Perceptualloss 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 whenvisually compared with other methods. The quantitative measures show ourproposed S-WGAN achieves the best Structure Similarity Index Measure (SSIM) of0.94.

Symmetric Skip Connection Wasserstein GAN for High-Resolution Facial Image Inpainting