Anisotropic Stroke Control for Multiple Artists Style Transfer

TL;DR

This paper introduces a novel multi-artist style transfer framework that preserves semantic details and style diversity using anisotropic stroke control and multi-scale texture discrimination within a single model.

Contribution

It proposes a multi-condition generator with an anisotropic stroke module and a multi-scale discriminator for improved multi-artist style transfer.

Findings

Effective preservation of semantic information in style transfer.

Ability to transfer multiple artistic styles with a single model.

Results demonstrate distinctive artistic styles with semantic consistency.

Abstract

Though significant progress has been made in artistic style transfer, semantic information is usually difficult to be preserved in a fine-grained locally consistent manner by most existing methods, especially when multiple artists styles are required to transfer within one single model. To circumvent this issue, we propose a Stroke Control Multi-Artist Style Transfer framework. On the one hand, we develop a multi-condition single-generator structure which first performs multi-artist style transfer. On the one hand, we design an Anisotropic Stroke Module (ASM) which realizes the dynamic adjustment of style-stroke between the non-trivial and the trivial regions. ASM endows the network with the ability of adaptive semantic-consistency among various styles. On the other hand, we present an novel Multi-Scale Projection Discriminator} to realize the texture-level conditional generation. In contrast to the single-scale conditional discriminator, our discriminator is able to capture multi-scale texture clue to effectively distinguish a wide range of artistic styles. Extensive experimental results well demonstrate the feasibility and effectiveness of our approach. Our framework can transform a photograph into different artistic style oil painting via only ONE single model. Furthermore, the results are with distinctive artistic style and retain the anisotropic semantic information. The code is already available on github: https://github.com/neuralchen/ASMAGAN.