Papers › 6D Rotation Representation For Unconstrained Head Pose Estimation
6D Rotation Representation For Unconstrained Head Pose Estimation
Thorsten Hempel, Ahmed A. Abdelrahman, Ayoub Al-Hamadi
In this paper, we present a method for unconstrained end-to-end head pose estimation. We address the problem of ambiguous rotation labels by introducing the rotation matrix formalism for our ground truth data and propose a continuous 6D rotation matrix representation for efficient and robust direct regression. This way, our method can learn the full rotation appearance which is contrary to previous approaches that restrict the pose prediction to a narrow-angle for satisfactory results. In addition, we propose a geodesic distance-based loss to penalize our network with respect to the SO(3) manifold geometry. Experiments on the public AFLW2000 and BIWI datasets demonstrate that our proposed method significantly outperforms other state-of-the-art methods by up to 20\%. We open-source our training and testing code along with our pre-trained models: https://github.com/thohemp/6DRepNet.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Head Pose Estimation | AFLW2000 | 6DRepNet | MAE | 3.97 | #11 of 25 | Archive leaderboard | report |
| Head Pose Estimation | BIWI | 6DRepNet | MAE (trained with BIWI data) | 2.66 | #5 of 29 | Archive leaderboard | report |
| Head Pose Estimation | BIWI | 6DRepNet | MAE (trained with other data) | 3.47 | #5 of 29 | Archive leaderboard | report |
| Head Pose Estimation | Panoptic | 6DRepNet | Geodesic Error (GE) | 8.08 | #3 of 6 | 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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