@inproceedings{
qiu2024learning,
title={Learning Realistic Sketching: A Dual-agent Reinforcement Learning Approach},
author={Ji Qiu and Peng Lu and Xujun Peng and Wenhao Guo and Zhaoran Zhao and XiangTao Dong},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=fLbdDspNW3}
}
This paper presents a pioneering method for teaching computer sketching that transforms input images into sequential, parameterized strokes. However, two challenges are raised for this sketching task: weak stimuli during stroke decomposition and maintaining semantic correctness, stylistic consistency, and detail integrity in the final drawings. To tackle the challenge of weak stimuli, our method incorporates an attention agent, which enhances the algorithm's sensitivity to subtle canvas changes by focusing on smaller, magnified areas. Moreover, in enhancing the perceived quality of drawing outcomes, we integrate a sketching style feature extractor to seamlessly capture semantic information and execute style adaptation, alongside a drawing agent that decomposes strokes under the guidance of the XDoG reward, thereby ensuring the integrity of sketch details. Based on dual intelligent agents, we have constructed an efficient sketching model. Experimental results attest to the superiority of our approach in both visual effects and perceptual metrics when compared to state-of-the-art techniques, confirming its efficacy in achieving realistic sketching.
Learning Realistic Sketching: A Dual-agent Reinforcement Learning Approach
Ji Qiu Peng Lu Xujun Peng Wenhao Guo Zhaoran Zhao XiangTao Dong