# Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks

Source: [Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks](<https://www.sota2.com/research/paper/68fa62601e209131ce9f8eab>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:18:31.734Z

Authors: Alex J. Champandard

Published: 2016-03-05T23:09:51.000Z

## Abstract

Convolutional neural networks (CNNs) have proven highly effective at image synthesis and style transfer. For most users, however, using them as tools can be a challenging task due to their unpredictable behavior that goes against common intuitions. This paper introduces a novel concept to augment such generative architectures with semantic annotations, either by manually authoring pixel labels or using existing solutions for semantic segmentation. The result is a content-aware generative algorithm that offers meaningful control over the outcome. Thus, we increase the quality of images generated by avoiding common glitches, make the results look significantly more plausible, and extend the functional range of these algorithms---whether for portraits or landscapes, etc. Applications include semantic style transfer and turning doodles with few colors into masterful paintings!

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1603.01768>)

[PDF](<https://arxiv.org/pdf/1603.01768.pdf>)