Sketch & Paint: Stroke-by-Stroke Evolution of Visual Artworks
Jeripothula Prudviraj, Vikram Jamwal

TL;DR
This paper presents a novel approach to approximate the stroke-by-stroke evolution of artworks by converting images into vector forms and applying proximity-based clustering to infer plausible stroke sequences, aiding art understanding and reconstruction.
Contribution
The paper introduces a new method combining vectorization and clustering to estimate stroke sequences in artworks, even for unknown or diverse image types.
Findings
Effective stroke sequence inference demonstrated on WikiArt data.
Robustness across various image types including sketches and photographs.
Qualitative results show plausible and meaningful stroke order reconstructions.
Abstract
Understanding the stroke-based evolution of visual artworks is useful for advancing artwork learning, appreciation, and interactive display. While the stroke sequence of renowned artworks remains largely unknown, formulating this sequence for near-natural image drawing processes can significantly enhance our understanding of artistic techniques. This paper introduces a novel method for approximating artwork stroke evolution through a proximity-based clustering mechanism. We first convert pixel images into vector images via parametric curves and then explore the clustering approach to determine the sequence order of extracted strokes. Our proposed algorithm demonstrates the potential to infer stroke sequences in unknown artworks. We evaluate the performance of our method using WikiArt data and qualitatively demonstrate the plausible stroke sequences. Additionally, we demonstrate the…
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Taxonomy
TopicsAesthetic Perception and Analysis · Generative Adversarial Networks and Image Synthesis · Visual Attention and Saliency Detection
