Vectorized Region Based Brush Strokes for Artistic Rendering
This addresses the challenge for artists and educators in creating more artistically aligned and educational stroke-based renderings, though it appears incremental as it builds on existing stroke-based systems.
The paper tackles the problem of stroke-based painting systems struggling to produce stroke compositions that align with artistic principles by introducing an image-to-painting method that uses semantic guidance for brush strokes in targeted regions, computes stroke parameters, and establishes a rendering sequence, resulting in high fidelity and superior stroke quality across various input image types.
Creating a stroke-by-stroke evolution process of a visual artwork tries to bridge the emotional and educational gap between the finished static artwork and its creation process. Recent stroke-based painting systems focus on capturing stroke details by predicting and iteratively refining stroke parameters to maximize the similarity between the input image and the rendered output. However, these methods often struggle to produce stroke compositions that align with artistic principles and intent. To address this, we explore an image-to-painting method that (i) facilitates semantic guidance for brush strokes in targeted regions, (ii) computes the brush stroke parameters, and (iii) establishes a sequence among segments and strokes to sequentially render the final painting. Experimental results on various input image types, such as face images, paintings, and photographic images, show that our method aligns with a region-based painting strategy while rendering a painting with high fidelity and superior stroke quality.