This is the official repository of the paper
Abhijay Ghildyal, Li-Yun Wang, and Feng Liu.
In ICCV AI4VA workshop, 2025 (Oral). Please checkout the paper on [Arxiv]
Wölfflin's five principles offer a structured approach to analyzing stylistic variations for formal analysis. However, no existing metric effectively predicts all five principles in visual art. Computationally evaluating the visual aspects of a painting requires a metric that can interpret key elements such as color, composition, and thematic choices. Recent advancements in vision-language models (VLMs) have demonstrated their ability to evaluate abstract image attributes, making them promising candidates for this task. In this work, we investigate whether CLIP, pre-trained on large-scale data, can understand and predict Wölfflin's principles. Our findings indicate that it does not inherently capture such nuanced stylistic elements. To address this, we fine-tune CLIP on annotated datasets of real art images to predict a score for each principle. We evaluate our model, WP-CLIP, on GAN-generated paintings and the Pandora-18K art dataset, demonstrating its ability to generalize across diverse artistic styles. Our results highlight the potential of VLMs for automated art analysis.
# install requirements
pip install torch
pip install torchvision
pip install openai-clip
# download model
pip install gdown
gdown 1IkAmA2pIyiMTWVgg-W1U193Zd3-MwOeQ --output ./ckpts/
# run on a sample image
python test_single_image.py -i samples/26987.jpg
For detailed installation instructions, check CLIP-IQA.
The model weights can be downloaded from this link
If you find this repository useful for your research, please cite the following paper.
@inproceedings{ghildyal2025wpclip,
title={WP-CLIP: Leveraging CLIP to Predict Wölfflin's Principles in Visual Art},
author={Abhijay Ghildyal and Li-Yun Wang and Feng Liu},
booktitle={International Conference on Computer Vision (ICCV) AI for Visual Arts Workshop},
year={2025}
}