Unsupervised Discovery of Facial Landmarks and Head Pose
Tourani, Satyajit Tourani, Siddharth Mahmood, Arif Khan, Muhammad Haris
Tourani, Satyajit
Tourani, Siddharth
Mahmood, Arif
Khan, Muhammad Haris
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Unsupervised landmark and head pose estimation is fundamental in fields like biometrics, augmented reality, and emotion recognition, offering accurate spatial data without relying on labeled datasets. It enhances scalability, adaptability, and generalization across diverse settings, where manual labeling is costly. In this work we exploit Stable Diffusion to approach the challenging problem of unsupervised landmarks and head pose estimation and make following contributions. (a) We propose a semantic-aware landmark localization algorithm including a consistent landmarks selection technique. (b) To encode landmarks and their holistic configuration, we propose learning image-aware textual embedding. (c) A novel algorithm for landmarks-guided 3D head pose estimation is also proposed. (d) We refine the landmarks using head pose by innovating a 3D rendering based augmentation and pose-based batching technique while the refined landmarks, consequently improving the head pose. (e) We report a new state-of-the-art in unsupervised facial landmark estimation across five challenging datasets including AFLW2000, MAFL, Cat-Heads, LS3D and a facial landmark tracking benchmark 300VW. In unsupervised head pose estimation, we outperform existing methods on BIWI and AFLW2000 by visible margins. Moreover, our method provides a significant training speed-up over the existing best unsupervised landmark detection method.
Citation
S. Tourani, S. Tourani, A. Mahmood, and M. H. Khan, “Unsupervised Discovery of Facial Landmarks and Head Pose,” 2025. Accessed: Jun. 24, 2025. [Online]. Available: https://github.com/touranisatyajit/cvpr2025-uld-
Source
Proceedings of the Computer Vision and Pattern Recognition Conference
Conference
Computer Vision and Pattern Recognition Conference (CVPR), 2025
Keywords
Subjects
Source
Computer Vision and Pattern Recognition Conference (CVPR), 2025
Publisher
Computer Vision Foundation