Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes

By Dmytro Kotovenko, Matthias Wright, Arthur Heimbrecht, Björn Ommer. 2021

80 citations recorded by the source corpus.

There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However, we argue that this representation is unnatural because paintings usually consist of brushstrokes rather than pixels. We propose a method to stylize images by optimizing parameterized brushstrokes instead of pixels and further introduce a simple differentiable rendering mechanism.Our approach significantly improves visual quality and enables additional control over the stylization process such as controlling the flow of brushstrokes through user input.We provide qualitative and quantitative evaluations that show the efficacy of the proposed parameterized representation. Code is available at https://github.com/CompVis/brushstroke-parameterized-style-transfer.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

1. Kevin Frans
2. Olaf Witkowski
3. Lisa B. Soros

In Computer Vision and Pattern Recognition

1. Kevin Frans
2. Olaf Witkowski
3. Lisa B. Soros

Citations in the corpus

• CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders (2021, background): “Image generation methods that operate over vector images have traditionally required a vector-based dataset, however recent work has shown how differentiable renderers can be used to bypass this limitation (Reddy et al., 2021; Shen and Chen, 2021; Kotovenko et al., 2021).”