{"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/towards-metrical-reconstruction-of-human","title":"Towards Metrical Reconstruction of Human Faces","arxiv_id":"2204.06607","date":"2022-04-13","proceeding":null,"authors":["Wojciech Zielonka","Timo Bolkart","Justus Thies"],"abstract":"Face reconstruction and tracking is a building block of numerous applications in AR/VR, human-machine interaction, as well as medical applications. Most of these applications rely on a metrically correct prediction of the shape, especially, when the reconstructed subject is put into a metrical context (i.e., when there is a reference object of known size). A metrical reconstruction is also needed for any application that measures distances and dimensions of the subject (e.g., to virtually fit a glasses frame). State-of-the-art methods for face reconstruction from a single image are trained on large 2D image datasets in a self-supervised fashion. However, due to the nature of a perspective projection they are not able to reconstruct the actual face dimensions, and even predicting the average human face outperforms some of these methods in a metrical sense. To learn the actual shape of a face, we argue for a supervised training scheme. Since there exists no large-scale 3D dataset for this task, we annotated and unified small- and medium-scale databases. The resulting unified dataset is still a medium-scale dataset with more than 2k identities and training purely on it would lead to overfitting. To this end, we take advantage of a face recognition network pretrained on a large-scale 2D image dataset, which provides distinct features for different faces and is robust to expression, illumination, and camera changes. Using these features, we train our face shape estimator in a supervised fashion, inheriting the robustness and generalization of the face recognition network. Our method, which we call MICA (MetrIC fAce), outperforms the state-of-the-art reconstruction methods by a large margin, both on current non-metric benchmarks as well as on our metric benchmarks (15% and 24% lower average error on NoW, respectively).","url_abs":"https://arxiv.org/abs/2204.06607v2","url_pdf":"https://arxiv.org/pdf/2204.06607v2.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":"towards-metrical-reconstruction-of-human","repo_url":"https://github.com/Zielon/MICA","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"2k","task_name":"2k"},{"task_slug":"3d-face-reconstruction","task_name":"3D Face Reconstruction"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"face-reconstruction","task_name":"Face Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-face-reconstruction-on-now-benchmark-1","task":"3D Face Reconstruction","dataset":"NoW Benchmark","model":"MICA","rank_in_archive_order":2,"of":17,"metrics":{"Mean Reconstruction Error (mm)":"1.11","Median Reconstruction Error":"0.90","Stdev Reconstruction Error (mm)":"0.92"},"uses_additional_data":false},{"leaderboard":"/sota/3d-face-reconstruction-on-realy","task":"3D Face Reconstruction","dataset":"REALY","model":"MICA","rank_in_archive_order":19,"of":24,"metrics":{"@cheek":"1.099 (±0.324)","@forehead":"2.374 (±0.683)","@mouth":"3.478 (±1.204)","@nose":"1.585 (±0.325)","all":"2.134"},"uses_additional_data":false},{"leaderboard":"/sota/3d-face-reconstruction-on-realy-side-view","task":"3D Face Reconstruction","dataset":"REALY (side-view)","model":"MICA","rank_in_archive_order":15,"of":19,"metrics":{"@cheek":"1.109 (±0.325)","@forehead":"2.379 (±0.675)","@mouth":"3.567 (±1.212)","@nose":"1.525 (±0.322)","all":"2.145"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2204.06607","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}