{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/head-pose-estimation/papers/2","list_of":"/task/head-pose-estimation","task":"Head Pose Estimation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,130],"of":130,"counts":{"archive_papers_tagged":130,"with_a_code_link":55,"where_syntology_ran_a_sample":7,"not_listed_spam_title":0,"listed":130,"listed_where_code_ran":7,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":5,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":5,"listed_every_run_a_failure_of_syntologys_instrument":2,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/head-pose-estimation","prev":"/task/head-pose-estimation","next":null,"papers":[{"url":null,"slug":"towards-real-time-head-pose-estimation","title":"Towards Real-Time Head Pose Estimation: Exploring Parameter-Reduced Residual Networks on In-the-wild Datasets","date":"2019-06-12","arxiv_id":"1906.05203","repositories_listed":0,"syntology":null},{"url":null,"slug":"validation-loss-for-landmark-detection","title":"Learning to Validate the Quality of Detected Landmarks","date":"2019-01-29","arxiv_id":"1901.10143","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-deep-and-depth-deep-learning-and","title":"Combining Deep and Depth: Deep Learning and Face Depth Maps for Driver Attention Monitoring","date":"2018-12-14","arxiv_id":"1812.05831","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepgum-learning-deep-robust-regression-with","title":"DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model","date":"2018-08-28","arxiv_id":"1808.09211","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-head-motion-compensation-using-multi","title":"Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks","date":"2018-07-10","arxiv_id":"1807.03651","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-is-believing-pedestrian-trajectory","title":"“Seeing is Believing”: Pedestrian Trajectory Forecasting Using Visual Frustum of Attention","date":"2018-03-12","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"face-from-depth-for-head-pose-estimation-on","title":"Face-from-Depth for Head Pose Estimation on Depth Images","date":"2017-12-12","arxiv_id":"1712.05277","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-facial-landmark-detection-pose","title":"Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion","date":"2017-09-23","arxiv_id":"1709.08130","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-mixture-of-linear-inverse-regressions","title":"Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/dynamic-facial-analysis-from-bayesian","slug":"dynamic-facial-analysis-from-bayesian","title":"Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"from-depth-data-to-head-pose-estimation-a","title":"From Depth Data to Head Pose Estimation: a Siamese approach","date":"2017-03-10","arxiv_id":"1703.03624","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-head-pose-estimation-from-depth-data-for","title":"Deep Head Pose Estimation from Depth Data for In-car Automotive Applications","date":"2017-03-06","arxiv_id":"1703.01883","repositories_listed":0,"syntology":null},{"url":"/paper/kepler-keypoint-and-pose-estimation-of","slug":"kepler-keypoint-and-pose-estimation-of","title":"KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors","date":"2017-02-16","arxiv_id":"1702.05085","repositories_listed":0,"syntology":null},{"url":null,"slug":"poseidon-face-from-depth-for-driver-pose","title":"POSEidon: Face-from-Depth for Driver Pose Estimation","date":"2016-11-30","arxiv_id":"1611.10195","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-pose-robust-facial-expression","title":"Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests","date":"2016-07-21","arxiv_id":"1607.06250","repositories_listed":0,"syntology":null},{"url":null,"slug":"mnemonic-descent-method-a-recurrent-process","title":"Mnemonic Descent Method: A Recurrent Process Applied for End-To-End Face Alignment","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"highly-accurate-gaze-estimation-using-a","title":"Highly accurate gaze estimation using a consumer RGB-depth sensor","date":"2016-04-05","arxiv_id":"1604.01420","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-head-pose-estimation-based-on","title":"Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions","date":"2016-03-31","arxiv_id":"1603.09732","repositories_listed":0,"syntology":null},{"url":null,"slug":"head-pose-estimation-of-occluded-faces-using","title":"Head Pose Estimation of Occluded Faces using Regularized Regression","date":"2016-02-02","arxiv_id":"1602.00997","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-accurate-head-pose-estimation-via","title":"Fast and Accurate Head Pose Estimation via Random Projection Forests","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"regressing-a-3d-face-shape-from-a-single","title":"Regressing a 3D Face Shape From a Single Image","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-model-based-3d-head-pose-estimation","title":"Robust Model-Based 3D Head Pose Estimation","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/face-alignment-across-large-poses-a-3d","slug":"face-alignment-across-large-poses-a-3d","title":"Face Alignment Across Large Poses: A 3D Solution","date":"2015-11-23","arxiv_id":"1511.07212","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-arbitrary-view-face-alignment-by","title":"Towards Arbitrary-View Face Alignment by Recommendation Trees","date":"2015-11-20","arxiv_id":"1511.06627","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-head-pose-estimation-by-facial-features","title":"Human Head Pose Estimation by Facial Features Location","date":"2015-10-09","arxiv_id":"1510.02774","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-3d-head-pose-and-facial-landmark","title":"Real-Time 3D Head Pose and Facial Landmark Estimation From Depth Images Using Triangular Surface Patch Features","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-descriptor-learning-for-multi","title":"Supervised Descriptor Learning for Multi-Output Regression","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-s-hock-dataset-analyzing-crowds-at-the","title":"The S-Hock Dataset: Analyzing Crowds at the Stadium","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"head-pose-estimation-based-on-multivariate","title":"Head Pose Estimation Based on Multivariate Label Distribution","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"growing-regression-forests-by-classification","title":"Growing Regression Forests by Classification: Applications to Object Pose Estimation","date":"2013-12-22","arxiv_id":"1312.6430","repositories_listed":0,"syntology":null}],"record_sha256":"64e177414bb9db5ab7ebfa0ddaccaa605d0549f3603027c705913fafa87ddd91","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}