Papers › Unsupervised Learning of Object Landmarks through Conditional Image Generation
Unsupervised Learning of Object Landmarks through Conditional Image Generation
Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision. We cast this as the problem of generating images that combine the appearance of the object as seen in a first example image with the geometry of the object as seen in a second example image, where the two examples differ by a viewpoint change and/or an object deformation. In order to factorize appearance and geometry, we introduce a tight bottleneck in the geometry-extraction process that selects and distils geometry-related features. Compared to standard image generation problems, which often use generative adversarial networks, our generation task is conditioned on both appearance and geometry and thus is significantly less ambiguous, to the point that adopting a simple perceptual loss formulation is sufficient. We demonstrate that our approach can learn object landmarks from synthetic image deformations or videos, all without manual supervision, while outperforming state-of-the-art unsupervised landmark detectors. We further show that our method is applicable to a large variety of datasets - faces, people, 3D objects, and digits - without any modifications.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Facial Landmark Detection | AFLW (Zhang CVPR 2018 crops) | Conditional Image Generation | NME | 6.31 | #1 of 4 | Archive leaderboard | report |
| Unsupervised Facial Landmark Detection | MAFL | Conditional Image Generation | NME | 2.54 | #2 of 13 | Archive leaderboard | report |
| Unsupervised Facial Landmark Detection | MAFL Unaligned | IMM | NME | 8.74 | #3 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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