Papers › Fine-Grained Head Pose Estimation Without Keypoints
Fine-Grained Head Pose Estimation Without Keypoints
Nataniel Ruiz, Eunji Chong, James M. Rehg
Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment. Traditionally head pose is computed by estimating some keypoints from the target face and solving the 2D to 3D correspondence problem with a mean human head model. We argue that this is a fragile method because it relies entirely on landmark detection performance, the extraneous head model and an ad-hoc fitting step. We present an elegant and robust way to determine pose by training a multi-loss convolutional neural network on 300W-LP, a large synthetically expanded dataset, to predict intrinsic Euler angles (yaw, pitch and roll) directly from image intensities through joint binned pose classification and regression. We present empirical tests on common in-the-wild pose benchmark datasets which show state-of-the-art results. Additionally we test our method on a dataset usually used for pose estimation using depth and start to close the gap with state-of-the-art depth pose methods. We open-source our training and testing code as well as release our pre-trained models.
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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 | AFLW | Ruiz et al. | MAE | 5.324 | #5 of 6 | Archive leaderboard | report |
| Head Pose Estimation | AFLW2000 | Hopenet | Geodesic Error (GE) | 9,93 | #21 of 25 | Archive leaderboard | report |
| Head Pose Estimation | AFLW2000 | Hopenet | MAE | 6.15 | #21 of 25 | Archive leaderboard | report |
| Head Pose Estimation | AFLW2000 | Multi-Loss ResNet50 (a=2) | MAE | 6.155 | #22 of 25 | Archive leaderboard | report |
| Head Pose Estimation | BIWI | hopenet | Geodesic Error (GE) | 9.53 | #18 of 29 | Archive leaderboard | report |
| Head Pose Estimation | BIWI | hopenet | Geodesic Error - aligned (GE) | 6.6 | #18 of 29 | Archive leaderboard | report |
| Head Pose Estimation | BIWI | hopenet | MAE (trained with other data) | 4.89 | #18 of 29 | Archive leaderboard | report |
| Head Pose Estimation | BIWI | hopenet | MAE-aligned (trained with other data) | 3.48 | #18 of 29 | Archive leaderboard | report |
| Head Pose Estimation | BIWI | Multi-Loss ResNet50 | MAE (trained with BIWI data) | 4.895 | #28 of 29 | 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.
Methods
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