Controllable Multi-domain Semantic Artwork Synthesis

• URL: http://arxiv.org/abs/2308.10111v1
• Date: Sat, 19 Aug 2023 21:16:28 GMT
• Title: Controllable Multi-domain Semantic Artwork Synthesis
• Authors: Yuantian Huang, Satoshi Iizuka, Edgar Simo-Serra, and Kazuhiro Fukui
• Abstract summary: We propose a dataset that contains 40,000 images of artwork from 4 different domains with their corresponding semantic label maps. We generate the dataset by first extracting semantic maps from landscape photography. We then propose a conditional Generative Adrial Network (GAN)-based approach to generate high-quality artwork.
• Score: 17.536225601718687
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel framework for multi-domain synthesis of artwork from semantic layouts. One of the main limitations of this challenging task is the lack of publicly available segmentation datasets for art synthesis. To address this problem, we propose a dataset, which we call ArtSem, that contains 40,000 images of artwork from 4 different domains with their corresponding semantic label maps. We generate the dataset by first extracting semantic maps from landscape photography and then propose a conditional Generative Adversarial Network (GAN)-based approach to generate high-quality artwork from the semantic maps without necessitating paired training data. Furthermore, we propose an artwork synthesis model that uses domain-dependent variational encoders for high-quality multi-domain synthesis. The model is improved and complemented with a simple but effective normalization method, based on normalizing both the semantic and style jointly, which we call Spatially STyle-Adaptive Normalization (SSTAN). In contrast to previous methods that only take semantic layout as input, our model is able to learn a joint representation of both style and semantic information, which leads to better generation quality for synthesizing artistic images. Results indicate that our model learns to separate the domains in the latent space, and thus, by identifying the hyperplanes that separate the different domains, we can also perform fine-grained control of the synthesized artwork. By combining our proposed dataset and approach, we are able to generate user-controllable artwork that is of higher quality than existing

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