Conditional GANs for painting generation

Adeel Mufti; Biagio Antonelli; Julius Monello

doi:10.1117/12.2556551

31 January 2020 Conditional GANs for painting generation

Adeel Mufti, Biagio Antonelli, Julius Monello

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143335 (2020) https://doi.org/10.1117/12.2556551
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

We examined the use of modern Generative Adversarial Networks to generate novel images of oil paintings using the Painter By Numbers dataset. We implemented Spectral Normalization GAN (SN-GAN), and compared its outputs to a Deep Convolutional GAN. Visually, and quantitatively according to the Sliced Wasserstein Distance metric, we determined that the SN-GAN produced paintings that were most comparable to our training dataset. We then performed a series of experiments to add supervised conditioning to SN-GAN, the culmination of which is what we believe to be a novel architecture that can generate face paintings with user-specified characteristics.

Citation Download Citation

Adeel Mufti, Biagio Antonelli, and Julius Monello "Conditional GANs for painting generation", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143335 (31 January 2020); https://doi.org/10.1117/12.2556551

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