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arXiv:2208.01748cs.CV

A Fast Text-Driven Approach for Generating Artistic Content

Marian Lupascu, Ryan Murdock, Ionut Mironica, Yijun Li

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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.

arXiv comments
3 pages, 2 figures
Journal reference
published in Siggraph 2022
DOI
doi:10.1145/3532719.3543208
Subjects
cs.CVComputer Vision and Pattern Recognition cs.MMMultimedia

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