Summary of Processpainter: Learn Painting Process From Sequence Data, by Yiren Song et al.
ProcessPainter: Learn Painting Process from Sequence Data
by Yiren Song, Shijie Huang, Chen Yao, Xiaojun Ye, Hai Ci, Jiaming Liu, Yuxuan Zhang, Mike Zheng Shou
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ProcessPainter model leverages a text-to-video approach to generate detailed, step-by-step painting processes for artists, addressing limitations in traditional stroke-based rendering methods and text-to-image models utilizing diffusion processes. The model is initially pre-trained on synthetic data and fine-tuned with a select set of artists’ painting sequences using the LoRA model. This yields successful generation of painting processes from text prompts for the first time. Additionally, an Artwork Replication Network is introduced, allowing for controlled generation of painting processes, decomposition of images into painting sequences, and completion of semi-finished artworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artists create paintings in a step-by-step process that varies among styles and painters. To help teach art and study the creative process, we need to understand how artists paint. Current methods can’t accurately mimic this process. A new model called ProcessPainter uses text-to-video technology to generate painting steps from a description of what to paint. This is the first time this has been done successfully. The model learns by looking at examples of artist’s paintings and then tries to replicate those steps. We also developed a way to take an unfinished painting and add more details, like completing a partially painted picture. |
Keywords
» Artificial intelligence » Diffusion » Lora » Synthetic data
