Papers › 2D Image head pose estimation via latent space regression under occlusion settings

2D Image head pose estimation via latent space regression under occlusion settings

10 Nov 2023arXiv:2311.06038archive 2025-07-28

José Celestino, Manuel Marques, Jacinto C. Nascimento, João Paulo Costeira

Head orientation is a challenging Computer Vision problem that has been extensively researched having a wide variety of applications. However, current state-of-the-art systems still underperform in the presence of occlusions and are unreliable for many task applications in such scenarios. This work proposes a novel deep learning approach for the problem of head pose estimation under occlusions. The strategy is based on latent space regression as a fundamental key to better structure the problem for occluded scenarios. Our model surpasses several state-of-the-art methodologies for occluded HPE, and achieves similar accuracy for non-occluded scenarios. We demonstrate the usefulness of the proposed approach with: (i) two synthetically occluded versions of the BIWI and AFLW2000 datasets, (ii) real-life occlusions of the Pandora dataset, and (iii) a real-life application to human-robot interaction scenarios where face occlusions often occur. Specifically, the autonomous feeding from a robotic arm.

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Head Pose EstimationPose Estimationregression

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Head Pose Estimation AFLW2000 LSR MAE 4.412 #14 of 25 Archive leaderboard report
Head Pose Estimation BIWI LSR MAE (trained with other data) 3.519 #7 of 29 Archive leaderboard report

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