  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.
