Brush stroke synthesis with a generative adversarial network driven by physically based simulation
Rundong Wu, Zhili Chen, Zhaowen Wang, Jimei Yang, Steve Marschner · 2018
We introduce a novel approach that uses a generative adversarial network (GAN) to synthesize realistic oil painting brush strokes, where the network is trained with data generated by a high-fidelity simulator. Among approaches to digitally synthesizing natural media painting strokes, physically based simulation produces by far the most realistic visual results and allows the most intuitive control of stroke variations. However, accurate physics simulations are known to be computationally expensive and often cannot meet the performance requirements of painting applications.