Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes
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 (2)
Tasks
Style TransferSimilar Papers 제목 키워드 기반
Multi-view Arbitrary Style Transfer
In this paper, we introduce pioneering algorithms for multi-view arbitrary style transfer. Multi-view arbitrary style transfer is an advanced study of the conventional monocular arbitrary style transfer, which aims to pr…
Style TransferNeural Painters: A learned differentiable constraint for generating brushstroke paintings
We explore neural painters, a generative model for brushstrokes learned from a real non-differentiable and non-deterministic painting program. We show that when training an agent to "paint" images using brushstrokes, usi…
Style TransferArtistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists
Robotic painting has been a subject of interest among both artists and roboticists since the 1970s. Researchers and interdisciplinary artists have employed various painting techniques and human-robot collaboration models…
BIG-bench Machine LearningStyle TransferParameterized Brushstroke Style Transfer
Computer Vision-based Style Transfer techniques have been used for many years to represent artistic style. However, most contemporary methods have been restricted to the pixel domain; in other words, the style transfer a…
Style TransferInteractive Multi-level Stroke Control for Neural Style Transfer
We present StyleTune, a mobile app for interactive multi-level control of neural style transfers that facilitates creative adjustments of style elements and enables high output fidelity. In contrast to current mobile neu…
Style Transfer