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

Understand

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.