Any Fashion Attribute Editing: Dataset and Pretrained Models
Shumin Zhu, Xingxing Zou, Wenhan Yang, Wai Keung Wong · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2025
Fashion attribute editing is essential for combining the expertise of fashion designers with the potential of generative artificial intelligence. In this work, we focus on 'any' fashion attribute editing: 1) the ability to edit 78 fine-grained design attributes commonly observed in daily life; 2) the capability to modify desired attributes while keeping the rest components still; and 3) the flexibility to continuously edit on the edited image. To this end, we present the Any Fashion Attribute Editing (AFED) dataset, which includes 830 K high-quality fashion images from sketch and product domains, filling the gap for a large-scale, openly accessible fine-grained dataset. We also propose Twin-Net, a twin encoder-decoder GAN inversion method that offers diverse and precise information for high-fidelity image reconstruction. This inversion model, trained on the new dataset, serves as a robust foundation for attribute editing. Additionally, we introduce PairsPCA to identify semantic directions in latent space, enabling accurate editing without manual supervision. Comprehensive experiments, including comparisons with ten state-of-the-art image inversion methods and four editing algorithms, demonstrate the effectiveness of our Twin-Net and editing algorithm. All data and models are available at https://github.com/ArtmeScienceLab/AnyFashionAttributeEditing.