Assistive drawing aims to facilitate the creative process by providing intelligent guidance to artists. Existing solutions often fail to effectively model intricate stroke details or adequately address the temporal aspects of drawing. We introduce hyperstroke, a novel stroke representation designed to capture precise fine stroke details, including RGB appearance and alpha-channel opacity. Using a Vector Quantization approach, hyperstroke learns compact tokenized representations of strokes from real-life drawing videos of artistic drawing. With hyperstroke, we propose to model assistive drawing via a transformer-based architecture, to enable intuitive and user-friendly drawing applications, which are experimented in our exploratory evaluation.
@article{arxiv.2408.09348,
title = {Hyperstroke: A Novel High-quality Stroke Representation for Assistive Artistic Drawing},
author = {Haoyun Qin and Jian Lin and Hanyuan Liu and Xueting Liu and Chengze Li},
journal= {arXiv preprint arXiv:2408.09348},
year = {2024}
}