This repository contains the project page for UDT, accepted at BMVC 2025.
The project page is implemented in static HTML/CSS/JS and can be deployed on GitHub Pages or any static web server.
- Project Website: Project Page
- Paper (arXiv PDF): PDF
- Supplementary Material: PDF
Diffusion models achieve impressive image synthesis, yet unsupervised methods for latent space exploration remain limited in fine-grained class translation. Existing approaches struggle with fine-grained class translation, often producing low-diversity outputs within parent classes or inconsistent child-class mappings across images. We propose UDT (Unsupervised Discovery of Transformations), a framework that incorporates hierarchical structure into unsupervised direction discovery. UDT leverages parent-class prompts to decompose predicted noise into class-general and class-specific components, ensuring translations remain within the parent domain while enabling disentangled child-class transformations. A hierarchy-aware contrastive loss further enforces consistency, with each direction corresponding to a distinct child class. Experiments on dogs, cats, birds, and flowers show that UDT outperforms state-of-the-art methods both qualitatively and quantitatively. Moreover, UDT supports controllable interpolation, allowing for the smooth generation of intermediate classes (e.g., mixed breeds). These results demonstrate UDT as a general and effective solution for fine-grained image translation.