Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes
Dmytro Kotovenko, Matthias Wright, Arthur Heimbrecht, Björn Ommer
摘要
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 -parameterizedstyle-transfer.
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引用它的顶会 Paper21
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它引用的顶会 Paper8
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- U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image TranslationJunho Kim, Minjae Kim, Hyeonwoo Kang, Kwanghee LeeICLR 2020 · 被引用 632 次
- Content and Style Disentanglement for Artistic Style TransferDmytro Kotovenko, Artsiom Sanakoyeu, Sabine Lang, Björn OmmerICCV 2019 · 被引用 187 次
- Learning to Paint With Model-Based Deep Reinforcement LearningZhewei Huang, Shuchang Zhou, Wen HengICCV 2019 · 被引用 180 次
- Diversified Arbitrary Style Transfer via Deep Feature PerturbationZhizhong Wang, Lei Zhao, Haibo Chen, Lihong Qiu 等CVPR 2020
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