A Fast Text-Driven Approach for Generating Artistic Content
Marian Lupascu, Ryan Murdock, Ionut Mironica, Yijun Li

TL;DR
This paper introduces a fast, flexible framework for generating artistic visual content from text, capable of producing diverse styles and details with improved speed and enhanced artistic features through super-resolution.
Contribution
The work presents a novel, unrestricted text-driven art generation method with an integrated super-resolution module for detailed artistic outputs.
Findings
Supports multiple styles and content variations
Achieves faster generation speeds
Enhances artistic detail with super-resolution
Abstract
In this work, we propose a complete framework that generates visual art. Unlike previous stylization methods that are not flexible with style parameters (i.e., they allow stylization with only one style image, a single stylization text or stylization of a content image from a certain domain), our method has no such restriction. In addition, we implement an improved version that can generate a wide range of results with varying degrees of detail, style and structure, with a boost in generation speed. To further enhance the results, we insert an artistic super-resolution module in the generative pipeline. This module will bring additional details such as patterns specific to painters, slight brush marks, and so on.
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Video Analysis and Summarization · Computer Graphics and Visualization Techniques
