B-BSMG: Bézier Brush Stroke Model-Based Generator for Robotic Chinese Calligraphy

Abstract

Abstract In robotic Chinese calligraphy, the brush stroke training models for Chinese hairy brushes play a crucial role in stroke generation. The method of combining end-to-end techniques and physical models requires further study, however, it is difficult to obtain large amounts of brush strokes for deep learning and training. To overcome this, we propose using a simulated brush model to train a generator based on the Bézier brush stroke model generator (B-BSMG), which was formed by two symmetric cubic Bézier curves according to the physical characteristics and writing posture of the brush. The B-BSMG can generate images for deep learning and training using a dataset simulated by the Bézier brush stroke model. Our renderer is based on parameterized brush strokes, providing a better foundation for deep learning or robotic writing. The results of several experiments prove that the proposed B-BSMG can generate stroke graphics well and outperforms other advanced stroke models.

Keywords

• Stroke training model
• Chinese brush
• Robotic Chinese calligraphy
• Brush stroke neural renderer