{"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/super-realtime-facial-landmark-detection-and","title":"Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters","arxiv_id":"1902.03459","date":"2019-02-09","proceeding":null,"authors":["Marcin Kopaczka","Justus Schock","Dorit Merhof"],"abstract":"We present a method for highly efficient landmark detection that combines\ndeep convolutional neural networks with well established model-based fitting\nalgorithms. Motivated by established model-based fitting methods such as active\nshapes, we use a PCA of the landmark positions to allow generative modeling of\nfacial landmarks. Instead of computing the model parameters using iterative\noptimization, the PCA is included in a deep neural network using a novel layer\ntype. The network predicts model parameters in a single forward pass, thereby\nallowing facial landmark detection at several hundreds of frames per second.\nOur architecture allows direct end-to-end training of a model-based landmark\ndetection method and shows that deep neural networks can be used to reliably\npredict model parameters directly without the need for an iterative\noptimization. The method is evaluated on different datasets for facial landmark\ndetection and medical image segmentation. PyTorch code is freely available at\nhttps://github.com/justusschock/shapenet","url_abs":"http://arxiv.org/abs/1902.03459v1","url_pdf":"http://arxiv.org/pdf/1902.03459v1.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":"super-realtime-facial-landmark-detection-and","repo_url":"https://github.com/justusschock/shapenet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"super-realtime-facial-landmark-detection-and","repo_url":"https://github.com/justusschock/shape-constrained-network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"facial-landmark-detection","task_name":"Facial Landmark Detection"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"pca","method_name":"PCA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}