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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.00870 |
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| _version_ | 1866910979769499648 |
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| author | Dev, Kapil |
| author_facet | Dev, Kapil |
| contents | Non-Photorealistic Rendering (NPR) has long been used to create artistic visualizations that prioritize style over realism, enabling the depiction of a wide range of aesthetic effects, from hand-drawn sketches to painterly renderings. While classical NPR methods, such as edge detection, toon shading, and geometric abstraction, have been well-established in both research and practice, with a particular focus on stroke-based rendering, the recent rise of deep learning represents a paradigm shift. We analyze the similarities and differences between classical and neural network based NPR techniques, focusing on stroke-based rendering (SBR), highlighting their strengths and limitations. We discuss trade offs in quality and artistic control between these paradigms, propose a framework where these approaches can be combined for new possibilities in expressive rendering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00870 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybridizing Expressive Rendering: Stroke-Based Rendering with Classic and Neural Methods Dev, Kapil Graphics Non-Photorealistic Rendering (NPR) has long been used to create artistic visualizations that prioritize style over realism, enabling the depiction of a wide range of aesthetic effects, from hand-drawn sketches to painterly renderings. While classical NPR methods, such as edge detection, toon shading, and geometric abstraction, have been well-established in both research and practice, with a particular focus on stroke-based rendering, the recent rise of deep learning represents a paradigm shift. We analyze the similarities and differences between classical and neural network based NPR techniques, focusing on stroke-based rendering (SBR), highlighting their strengths and limitations. We discuss trade offs in quality and artistic control between these paradigms, propose a framework where these approaches can be combined for new possibilities in expressive rendering. |
| title | Hybridizing Expressive Rendering: Stroke-Based Rendering with Classic and Neural Methods |
| topic | Graphics |
| url | https://arxiv.org/abs/2506.00870 |