{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/2d-image-head-pose-estimation-via-latent","title":"2D Image head pose estimation via latent space regression under occlusion settings","arxiv_id":"2311.06038","date":"2023-11-10","proceeding":null,"authors":["José Celestino","Manuel Marques","Jacinto C. Nascimento","João Paulo Costeira"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2311.06038v1","url_pdf":"https://arxiv.org/pdf/2311.06038v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"2d-image-head-pose-estimation-via-latent","repo_url":"https://github.com/sipg-isr/Occlusion_HPE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"head-pose-estimation","task_name":"Head Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/head-pose-estimation-on-aflw2000","task":"Head Pose Estimation","dataset":"AFLW2000","model":"LSR","rank_in_archive_order":14,"of":25,"metrics":{"MAE":"4.412"},"uses_additional_data":false},{"leaderboard":"/sota/head-pose-estimation-on-biwi","task":"Head Pose Estimation","dataset":"BIWI","model":"LSR","rank_in_archive_order":7,"of":29,"metrics":{"MAE (trained with other data)":"3.519"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}