2 research outputs found
A Fast Text-Driven Approach for Generating Artistic Content
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
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)