Stroke-based Rendering (SBR) aims to decompose an input image into a sequence of parameterized strokes, which can be rendered into a painting that resembles the input image. Recently, Neural Painting methods that utilize deep learning and reinforcement learning models to predict the stroke sequences have been developed, but suffer from longer inference time or unstable training. To address these issues, we propose AttentionPainter, an efficient and adaptive model for single-step neural painting. First, we propose a novel scalable stroke predictor, which predicts a large number of stroke parameters within a single forward process, instead of the iterative prediction of previous Reinforcement Learning or auto-regressive methods, which makes AttentionPainter faster than previous neural painting methods. To further increase the training efficiency, we propose a Fast Stroke Stacking algorithm, which brings 13 times acceleration for training. Moreover, we propose Stroke-density Loss, which encourages the model to use small strokes for detailed information, to help improve the reconstruction quality. Finally, we propose a new stroke diffusion model for both conditional and unconditional stroke-based generation, which denoises in the stroke parameter space and facilitates stroke-based inpainting and editing applications helpful for human artists design. Extensive experiments show that AttentionPainter outperforms the state-of-the-art neural painting methods.

翻译：基于笔触的渲染旨在将输入图像分解为一系列参数化笔触，这些笔触可被渲染成与输入图像相似的绘画作品。近期，利用深度学习和强化学习模型预测笔触序列的神经绘画方法已被提出，但存在推理时间较长或训练不稳定的问题。为解决这些问题，我们提出AttentionPainter，一种高效的单步神经绘画自适应模型。首先，我们提出一种新颖的可扩展笔触预测器，其在单次前向过程中预测大量笔触参数，而非如先前强化学习或自回归方法那样进行迭代预测，这使得AttentionPainter比以往的神经绘画方法更为快速。为进一步提升训练效率，我们提出快速笔触堆叠算法，实现了13倍的训练加速。此外，我们提出笔触密度损失函数，通过鼓励模型使用小笔触刻画细节信息，以提升重建质量。最后，我们提出一种新的笔触扩散模型，适用于条件与非条件的基于笔触的生成任务，该模型在笔触参数空间中进行去噪，有助于实现基于笔触的图像修复与编辑应用，为艺术家的创作提供支持。大量实验表明，AttentionPainter在性能上超越了当前最先进的神经绘画方法。