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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.12524 |
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| _version_ | 1866914974591352832 |
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| author | Sawada, Tomoya Katsurai, Marie |
| author_facet | Sawada, Tomoya Katsurai, Marie |
| contents | Stroke-based rendering aims to reconstruct an input image into an oil painting style by predicting brush stroke sequences. Conventional methods perform this prediction stroke-by-stroke or require multiple inference steps due to the limitations of a predictable number of strokes. This procedure leads to inefficient translation speed, limiting their practicality. In this study, we propose MambaPainter, capable of predicting a sequence of over 100 brush strokes in a single inference step, resulting in rapid translation. We achieve this sequence prediction by incorporating the selective state-space model. Additionally, we introduce a simple extension to patch-based rendering, which we use to translate high-resolution images, improving the visual quality with a minimal increase in computational cost. Experimental results demonstrate that MambaPainter can efficiently translate inputs to oil painting-style images compared to state-of-the-art methods. The codes are available at https://github.com/STomoya/MambaPainter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12524 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MambaPainter: Neural Stroke-Based Rendering in a Single Step Sawada, Tomoya Katsurai, Marie Computer Vision and Pattern Recognition Stroke-based rendering aims to reconstruct an input image into an oil painting style by predicting brush stroke sequences. Conventional methods perform this prediction stroke-by-stroke or require multiple inference steps due to the limitations of a predictable number of strokes. This procedure leads to inefficient translation speed, limiting their practicality. In this study, we propose MambaPainter, capable of predicting a sequence of over 100 brush strokes in a single inference step, resulting in rapid translation. We achieve this sequence prediction by incorporating the selective state-space model. Additionally, we introduce a simple extension to patch-based rendering, which we use to translate high-resolution images, improving the visual quality with a minimal increase in computational cost. Experimental results demonstrate that MambaPainter can efficiently translate inputs to oil painting-style images compared to state-of-the-art methods. The codes are available at https://github.com/STomoya/MambaPainter. |
| title | MambaPainter: Neural Stroke-Based Rendering in a Single Step |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.12524 |