Abstract Painting Synthesis via Decremental optimization
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Existing stroke-based painting synthesis methods usually fail to achieve good results with limited strokes because these methods use semantically irrelevant metrics to calculate the similarity between the painting and photo domains. Hence, it is hard to see meaningful semantical information from the painting. This paper proposes a painting synthesis method that uses a CLIP (Contrastive-Language-Image-Pretraining) model to build a semantically-aware metric so that the cross-domain semantic similarity is explicitly involved. To ensure the convergence of the objective function, we design a new strategy called decremental optimization. Specifically, we define painting as a set of strokes and use a neural renderer to obtain a rasterized painting by optimizing the stroke control parameters through a CLIP-based loss. The optimization process is initialized with an excessive number of brush strokes, and the number of strokes is then gradually reduced to generate paintings of varying levels of abstraction. Experiments show that our method can obtain vivid paintings, and the results are better than the comparison stroke-based painting synthesis methods when the number of strokes is limited.
Description
CCS Concepts: Computer Graphics → Non-photorealistic rendering; Stroke based rendering
@article{10.1111:cgf.14688,
journal = {Computer Graphics Forum},
title = {{Abstract Painting Synthesis via Decremental optimization}},
author = {Yan, Ming and Pu, Yuanyuan and Zhao, Pengzheng and Xu, Dan and Wu, Hao and Yang, Qiuxia and Wang, Ruxin},
year = {2022},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14688}
}