Controlling Geometric Abstraction and Texture for Artistic Images
Not on the map. Why?
Abstract (arXiv)
We present a novel method for the interactive control of geometric abstraction and texture in artistic images. Previous example-based stylization methods often entangle shape, texture, and color, while generative methods for image synthesis generally either make assumptions about the input image, such as only allowing faces or do not offer precise editing controls. By contrast, our holistic approach spatially decomposes the input into shapes and a parametric representation of high-frequency details comprising the image's texture, thus enabling independent control of color and texture. Each parameter in this representation controls painterly attributes of a pipeline of differentiable stylization filters. The proposed decoupling of shape and texture enables various options for stylistic editing, including interactive global and local adjustments of shape, stroke, and painterly attributes such as surface relief and contours. Additionally, we demonstrate optimization-based texture style-transfer in the parametric space using reference images and text prompts, as well as the training of single- and arbitrary style parameter prediction networks for real-time texture decomposition.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 46 references that are in the map. First posted on arXiv 31 Jul 2023.
Builds on
20 of 46 references.
References · 20
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
- 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
- 2021Vision Transformers for Dense Predictioncited by 1,789
- 2021CLIPstyler: Image Style Transfer with a Single Text Conditioncited by 203
- 2018Arbitrary Style Transfer with Style-Attentional Networkscited by 197
- 2019Style Transfer by Relaxed Optimal Transport and Self-Similaritycited by 167
- 2020Label Decoupling Framework for Salient Object Detectioncited by 111
8 more
- 2016Controlling Perceptual Factors in Neural Style Transfercited by 99
- 2019Learning to Paint With Model-based Deep Reinforcement Learningcited by 70
- 2021Paint Transformer: Feed Forward Neural Painting with Stroke Predictioncited by 61
- 2020Stylized Neural Paintingcited by 50
- 2018Stroke Controllable Fast Style Transfer with Adaptive Receptive Fieldscited by 33
- 2022Toward Modeling Creative Processes for Algorithmic Paintingcited by 9
- 2020NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraitscited by 6
- 2022WISE: Whitebox Image Stylization by Example-based Learningcited by 5
Built on by
Not cited yet.