The Style-Disentangled Transformer (SDT) separates writer and character style representations to generate online handwriting with conditional content and style. The model uses contrastive learning and Gaussian mixture models to disentangle subtle style variations between characters written by the same person. It extends to offline Chinese handwriting generation and was published at CVPR 2023.