DC-Art-GAN: Stable Procedural Content Generation using DC-GANs for Digital Art

• URL: http://arxiv.org/abs/2209.02847v1
• Date: Tue, 6 Sep 2022 23:06:46 GMT
• Title: DC-Art-GAN: Stable Procedural Content Generation using DC-GANs for Digital Art
• Authors: Rohit Gandikota and Nik Bear Brown
• Abstract summary: We advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation. The work mainly focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN) and explores the techniques to address the common pitfalls in GAN training.
• Score: 4.9631159466100305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Art is an artistic method of using digital technologies as a part of the generative or creative process. With the advent of digital currency and NFTs (Non-Fungible Token), the demand for digital art is growing aggressively. In this manuscript, we advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation. The work mainly focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN) and explores the techniques to address the common pitfalls in GAN training. We compare various architectures and designs of DC-GANs to arrive at a recommendable design choice for a stable and realistic generation. The main focus of the work is to generate realistic images that do not exist in reality but are synthesised from random noise by the proposed model. We provide visual results of generated animal face images (some pieces of evidence showing a blend of species) along with recommendations for training, architecture and design choices. We also show how training image preprocessing plays a massive role in GAN training.

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