Hyperstroke: A Novel High-quality Stroke Representation for Assistive Artistic Drawing
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 18 Aug 2024.
Builds on
10 of 14 references.
References · 10
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
- 2017A Neural Representation of Sketch Drawingscited by 311
- 2019Learning to Paint With Model-based Deep Reinforcement Learningcited by 70
- 2021Paint Transformer: Feed Forward Neural Painting with Stroke Predictioncited by 61
- 2024Lazy Diffusion Transformer for Interactive Image Editingcited by 19
- 2021Intelli-Paint: Towards Developing Human-like Painting Agentscited by 6
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Citing papers per year: 2026: 1.
1 paper.