一拍即合：100多笔刷一次生成油画，快得离谱
MambaPainter: Neural Stroke-Based Rendering in a Single Step
- 用状态空间模型一步预测超100笔刷序列
- 速度远超现有方法，高分辨率图也清晰
- 适合追求极速艺术化图像的开发者
笔刷渲染旨在通过预测笔刷序列将输入图像重构为油画风格。传统方法受限于可预测笔刷数量，需逐笔或分多步生成，导致翻译效率低下。本文提出MambaPainter，可在单次推理中预测超过100笔刷序列，实现快速转换。该方法引入选择性状态空间模型实现序列预测。此外，我们提出一种简单的基于块的渲染扩展，用于处理高分辨率图像，在计算成本几乎不变的前提下提升视觉质量。实验表明，相较于当前最优方法，MambaPainter在油绘画风图像生成上更具效率。代码已开源：https://github.com/STomoya/MambaPainter。
原文摘要 · Abstract (English)
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.
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