The painting process of artists is inherently stepwise and varies
significantly among different painters and styles. Generating detailed,
step-by-step painting processes is essential for art education and research,
yet remains largely underexplored. Traditional stroke-based rendering methods
break down images into sequences of brushstrokes, yet they fall short of
replicating the authentic processes of artists, with limitations confined to
basic brushstroke modifications. Text-to-image models utilizing diffusion
processes generate images through iterative denoising, also diverge
substantially from artists' painting process. To address these challenges, we
introduce ProcessPainter, a text-to-video model that is initially pre-trained
on synthetic data and subsequently fine-tuned with a select set of artists'
painting sequences using the LoRA model. This approach successfully generates
painting processes from text prompts for the first time. Furthermore, we
introduce an Artwork Replication Network capable of accepting arbitrary-frame
input, which facilitates the controlled generation of painting processes,
decomposing images into painting sequences, and completing semi-finished
artworks. This paper offers new perspectives and tools for advancing art
education and image generation technology.

通过合成数据预训练，在一组艺术家的绘画序列的细微调整下进行微调的文本到视频模型 ProcessPainter 首次成功地从文本提示生成了绘画过程。此外，引入了 Artwork Replication Network，能接受任意帧输入，促进了绘画过程的可控生成和艺术品的复制。本文提供了推动艺术教育和图像生成技术发展的新视角和工具。