{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:01:51Z","timestamp":1762351311227,"version":"3.37.3"},"reference-count":103,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,7,10]],"date-time":"2021-07-10T00:00:00Z","timestamp":1625875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,7,10]],"date-time":"2021-07-10T00:00:00Z","timestamp":1625875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Standard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches have emerged that reduce the registration of new scans to running inference with a previously-trained model. The potential benefits are multifold: inference is typically orders of magnitude faster than solving a new instance of a difficult optimization problem, deep learning models can be made robust to noise and corruption, and the trained model may be re-used for other tasks, e.g. through transfer learning. In this paper, we cast the registration task as a surface-to-surface translation problem, and design a model to reliably capture the latent geometric information directly from raw 3D face scans. We introduce Shape-My-Face (SMF), a powerful encoder-decoder architecture based on an improved point cloud encoder, a novel visual attention mechanism, graph convolutional decoders with skip connections, and a specialized mouth model that we smoothly integrate with the mesh convolutions. Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template. Additionally, our model provides topologically-sound meshes with minimal supervision, offers faster training time, has orders of magnitude fewer trainable parameters, is more robust to noise, and can generalize to previously unseen datasets. We extensively evaluate the quality of our registrations on diverse data. We demonstrate the robustness and generalizability of our model with in-the-wild face scans across different modalities, sensor types, and resolutions. Finally, we show that, by learning to register scans, SMF produces a hybrid linear and non-linear morphable model. Manipulation of the latent space of SMF allows for shape generation, and morphing applications such as expression transfer in-the-wild. We train SMF on a dataset of human faces comprising 9 large-scale databases on commodity hardware.<\/jats:p>","DOI":"10.1007\/s11263-021-01494-4","type":"journal-article","created":{"date-parts":[[2021,7,10]],"date-time":"2021-07-10T09:02:36Z","timestamp":1625907756000},"page":"2680-2713","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation"],"prefix":"10.1007","volume":"129","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2409-0261","authenticated-orcid":false,"given":"Mehdi","family":"Bahri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0525-3341","authenticated-orcid":false,"given":"Eimear","family":"O\u2019 Sullivan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8717-8722","authenticated-orcid":false,"given":"Shunwang","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2103-4659","authenticated-orcid":false,"given":"Feng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3215-8753","authenticated-orcid":false,"given":"Xiaoming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1262-7252","authenticated-orcid":false,"given":"Michael M.","family":"Bronstein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5222-1740","authenticated-orcid":false,"given":"Stefanos","family":"Zafeiriou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,10]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Abrevaya, V. F., Wuhrer, S., & Boyer, E. (2018). Multilinear autoencoder for 3D face model learning. In Proceedings\u20142018 IEEE winter conference on applications of computer vision, WACV 2018 (Vol. 2018, pp. 1\u20139).","key":"1494_CR1","DOI":"10.1109\/WACV.2018.00007"},{"doi-asserted-by":"crossref","unstructured":"Amberg, B., Romdhani, S., & Vetter, T. (2007). Optimal step nonrigid ICP algorithms for surface registration. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition, IEEE (pp. 1\u20138).","key":"1494_CR2","DOI":"10.1109\/CVPR.2007.383165"},{"doi-asserted-by":"crossref","unstructured":"Amberg, B., Knothe, R., & Vetter, T. (2008). Expression invariant 3D face recognition with a morphable model. In 2008 8th IEEE international conference on automatic face and gesture recognition, FG 2008.","key":"1494_CR3","DOI":"10.1109\/AFGR.2008.4813376"},{"doi-asserted-by":"crossref","unstructured":"Aoki, Y., Goforth, H., Srivatsan, R. A., & Lucey, S. (2019). Pointnetlk: Robust & efficient point cloud registration using pointnet. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition (Vol. 2019, pp. 7156\u20137165).","key":"1494_CR4","DOI":"10.1109\/CVPR.2019.00733"},{"doi-asserted-by":"crossref","unstructured":"Aytekin, C., Ni, X., Cricri, F., & Aksu, E. (2018). Clustering and unsupervised anomaly detection with l2 normalized deep auto-encoder representations. In Proceedings of the international joint conference on neural networks.","key":"1494_CR5","DOI":"10.1109\/IJCNN.2018.8489068"},{"doi-asserted-by":"crossref","unstructured":"Bagautdinov, T., Wu, C., Saragih, J., Fua, P., & Sheikh, Y. (2018). Modeling facial geometry using compositional VAEs. In The IEEE conference on computer vision and pattern recognition (CVPR).","key":"1494_CR6","DOI":"10.1109\/CVPR.2018.00408"},{"doi-asserted-by":"crossref","unstructured":"Bagdanov, A. D., Masi, I., & Del Bimbo, A. (2011). The florence 2D\/3D hybrid face datset. In Proceedings of ACM multimedia internationl workshop on multimedia access to 3D human objects (MA3HO\u201911). ACM, ACM Press.","key":"1494_CR7","DOI":"10.1145\/2072572.2072597"},{"issue":"6","key":"1494_CR8","first-page":"1009","volume":"46","author":"Y Baocai","year":"2009","unstructured":"Baocai, Y., Yanfeng, S., Chengzhang, W., & Yun, G. (2009). BJUT-3D large scale 3D face database and information processing. Journal of Computer Research and Development, 46(6), 1009.","journal-title":"Journal of Computer Research and Development"},{"issue":"2","key":"1494_CR9","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/34.121791","volume":"14","author":"PJ Besl","year":"1992","unstructured":"Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239\u2013256.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"doi-asserted-by":"crossref","unstructured":"Blanz, V., & Vetter, T. (1999). A morphable model for the synthesis of 3D faces. In Proceedings of the 26th annual conference on computer graphics and interactive techniques, SIGGRAPH 1999.","key":"1494_CR10","DOI":"10.1145\/311535.311556"},{"key":"1494_CR11","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1109\/TPAMI.2003.1227983","volume":"25","author":"V Blanz","year":"2003","unstructured":"Blanz, V., & Vetter, T. (2003). Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine, 25, 1063\u20131074. Intelligence.","journal-title":"IEEE Transactions on Pattern Analysis and Machine"},{"doi-asserted-by":"crossref","unstructured":"Bolkart, T., & Wuhrer, S. (2015). A groupwise multilinear correspondence optimization for 3D faces. In Proceedings of the IEEE international conference on computer vision.","key":"1494_CR12","DOI":"10.1109\/ICCV.2015.411"},{"key":"1494_CR13","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1109\/34.24792","volume":"11","author":"FL Bookstein","year":"1989","unstructured":"Bookstein, F. L. (1989). Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 567\u2013585.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"doi-asserted-by":"crossref","unstructured":"Booth, J., Roussos, A., Zafeiriou, S., Ponniahy, A., & Dunaway, D. (2016). A 3D morphable model learnt from 10,000 faces. In 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE (pp. 5543\u20135552).","key":"1494_CR14","DOI":"10.1109\/CVPR.2016.598"},{"doi-asserted-by":"crossref","unstructured":"Booth, J., Antonakos, E., Ploumpis, S., Trigeorgis, G., Panagakis, Y., & Zafeiriou, S. (2017). 3D face morphable models \u201cIn-the-Wild\u201d. In Proceedings\u201430th IEEE conference on computer vision and pattern recognition, CVPR 2017.","key":"1494_CR15","DOI":"10.1109\/CVPR.2017.580"},{"issue":"2\u20134","key":"1494_CR16","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s11263-017-1009-7","volume":"126","author":"J Booth","year":"2018","unstructured":"Booth, J., Roussos, A., Ponniah, A., Dunaway, D., & Zafeiriou, S. (2018a). Large scale 3D morphable models. International Journal of Computer Vision, 126(2\u20134), 233\u2013254.","journal-title":"International Journal of Computer Vision"},{"issue":"11","key":"1494_CR17","doi-asserted-by":"publisher","first-page":"2638","DOI":"10.1109\/TPAMI.2018.2832138","volume":"40","author":"J Booth","year":"2018","unstructured":"Booth, J., Roussos, A., Ververas, E., Antonakos, E., Ploumpis, S., Panagakis, Y., et al. (2018b). 3d reconstruction of \u201cin-the-wild\u201d faces in images and videos. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(11), 2638\u20132652.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"unstructured":"Boscaini, D., Masci, J., Rodol\u00e1, E., & Bronstein, M. (2016). Learning shape correspondence with anisotropic convolutional neural networks. In Proceedings of the 30th international conference on neural information processing systems (pp. 3197\u20133205).","key":"1494_CR18"},{"doi-asserted-by":"crossref","unstructured":"Bouritsas, G., Bokhnyak, S., Ploumpis, S., Bronstein, M., & Zafeiriou, S. (2019). Neural 3D morphable models: Spiral convolutional networks for 3D shape representation learning and generation. In The IEEE international conference on computer vision (ICCV).","key":"1494_CR19","DOI":"10.1109\/ICCV.2019.00731"},{"issue":"4","key":"1494_CR20","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","volume":"34","author":"MM Bronstein","year":"2017","unstructured":"Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017). Geometric deep learning: Going beyond Euclidean data. IEEE Signal Processing Magazine, 34(4), 18\u201342.","journal-title":"IEEE Signal Processing Magazine"},{"unstructured":"Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2014). Spectral networks and deep locally connected networks on graphs. In 2nd international conference on learning representations, ICLR 2014\u2014Conference track proceedings (pp. 1\u201314).","key":"1494_CR21"},{"doi-asserted-by":"crossref","unstructured":"Burt, P. J., & Adelsonm, E. H., (1985). Merging images through pattern decomposition. In Applications of digital image processing VIII.","key":"1494_CR22","DOI":"10.1117\/12.966501"},{"key":"1494_CR23","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1109\/TVCG.2013.249","volume":"20","author":"C Cao","year":"2014","unstructured":"Cao, C., Weng, Y., Zhou, S., Tong, Y., & Zhou, K. (2014). FaceWarehouse: A 3D facial expression database for visual computing. IEEE Transactions on Visualization and Computer Graphics, 20, 413\u2013425.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"doi-asserted-by":"crossref","unstructured":"Chen, Y., & Medioni, G. (1991). Object modeling by registration of multiple range images. In Proceedings\u2014IEEE international conference on robotics and automation (Vol. 3, pp. 2724\u20132729).","key":"1494_CR24","DOI":"10.1109\/ROBOT.1991.132043"},{"key":"1494_CR25","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.imavis.2016.10.007","volume":"58","author":"S Cheng","year":"2017","unstructured":"Cheng, S., Marras, I., Zafeiriou, S., & Pantic, M. (2017). Statistical non-rigid ICP algorithm and its application to 3d face alignment. Image and Vision Computing, 58, 3\u201312.","journal-title":"Image and Vision Computing"},{"doi-asserted-by":"crossref","unstructured":"Cheng, S., Kotsia, I., Pantic, M., & Zafeiriou, S. (2018). 4DFAB: A large scale 4D database for facial expression analysis and biometric applications. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition (pp. 5117\u20135126).","key":"1494_CR26","DOI":"10.1109\/CVPR.2018.00537"},{"unstructured":"Clevert, D. A., Unterthiner, T., & Hochreiter, S. (2016). Fast and accurate deep network learning by exponential linear units (ELUs). In 4th international conference on learning representations, ICLR 2016\u2014Conference track proceedings.","key":"1494_CR27"},{"issue":"11","key":"1494_CR28","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1145\/3131280","volume":"60","author":"K Crane","year":"2017","unstructured":"Crane, K., Weischedel, C., & Wardetzky, M. (2017). The heat method for distance computation. Communications of the ACM, 60(11), 90\u201399.","journal-title":"Communications of the ACM"},{"unstructured":"Crane, K., Vaz, C., & Fabri, A. (2020). The heat method. In CGAL user and reference manual (5th ed.). CGAL Editorial Board","key":"1494_CR29"},{"doi-asserted-by":"crossref","unstructured":"Dai, H., Pears, N., Smith, W., & Duncan, C. (2017). A 3D morphable model of craniofacial shape and texture variation. In Proceedings of the IEEE international conference on computer vision.","key":"1494_CR30","DOI":"10.1109\/ICCV.2017.335"},{"doi-asserted-by":"crossref","unstructured":"De Smet, M., & Van Gool, L. (2011). Optimal regions for linear model-based 3D face reconstruction. In Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics).","key":"1494_CR31","DOI":"10.1007\/978-3-642-19318-7_22"},{"unstructured":"Defferrard, M., Bresson, X., & Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 29, pp. 3844\u20133852). Curran Associates Inc.","key":"1494_CR32"},{"doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). ArcFace: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition.","key":"1494_CR33","DOI":"10.1109\/CVPR.2019.00482"},{"issue":"5","key":"1494_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3395208","volume":"39","author":"B Egger","year":"2020","unstructured":"Egger, B., Smith, W. A. P., Tewari, A., Wuhrer, S., Zollhoefer, M., Beeler, T., et al. (2020). 3D morphable face models-past, present, and future. ACM Transactions on Graphics, 39(5), 1\u201338. https:\/\/doi.org\/10.1145\/3395208.","journal-title":"ACM Transactions on Graphics"},{"doi-asserted-by":"crossref","unstructured":"Fey, M., Lenssen, J. E., Weichert, F., & Muller, H. (2018). SplineCNN: Fast geometric deep learning with continuous B-spline kernels. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition (pp. 869\u2013877)","key":"1494_CR35","DOI":"10.1109\/CVPR.2018.00097"},{"doi-asserted-by":"crossref","unstructured":"Feydy, J., Charlier, B., Vialard, F. X., & Peyr\u00e9, G. (2017). Optimal transport for diffeomorphic registration. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics).","key":"1494_CR36","DOI":"10.1007\/978-3-319-66182-7_34"},{"doi-asserted-by":"crossref","unstructured":"Garland, M., & Heckbert, PS. (1997). Surface simplification using quadric error metrics. In Proceedings of the 24th annual conference on computer graphics and interactive techniques. SIGGRAPH \u201997 (pp. 209\u2013216). ACM Press\/Addison-Wesley Publishing Co.","key":"1494_CR37","DOI":"10.1145\/258734.258849"},{"doi-asserted-by":"publisher","unstructured":"Gerig, T., Morel-Forster, A., Blumer, C., Egger, B., Luthi, M., Schoenborn, S., & Vetter, T. (2018). Morphable face models\u2014An open framework. In 2018 13th IEEE international conference on automatic face gesture recognition (FG 2018) (pp. 75\u201382). https:\/\/doi.org\/10.1109\/FG.2018.00021.","key":"1494_CR38","DOI":"10.1109\/FG.2018.00021"},{"unstructured":"Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017). Neural message passing for quantum chemistry. In 34th international conference on machine learning, ICML 2017 (Vol.\u00a03, pp. 2053\u20132070).","key":"1494_CR39"},{"doi-asserted-by":"crossref","unstructured":"Gong, S., Chen, L., Bronsteinm M., & Zafeiriou, S. (2019). SpiralNet++: A fast and highly efficient mesh convolution operator. In The IEEE international conference on computer vision (ICCV) workshops.","key":"1494_CR40","DOI":"10.1109\/ICCVW.2019.00509"},{"doi-asserted-by":"crossref","unstructured":"Gong, S., Bahri, M., Bronstein, MM., & Zafeiriou, S. (2020). Geometrically principled connections in graph neural networks. In IEEE\/CVF conference on computer vision and pattern recognition (CVPR).","key":"1494_CR41","DOI":"10.1109\/CVPR42600.2020.01143"},{"doi-asserted-by":"crossref","unstructured":"Gupta, S., Castleman, K. R., Markey, M. K., & Bovik, A. C. (2010). Texas 3D face recognition database. In Proceedings of the IEEE southwest symposium on image analysis and interpretation. IEEE (pp. 97\u2013100).","key":"1494_CR42","DOI":"10.1109\/SSIAI.2010.5483908"},{"unstructured":"Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. In Advances in neural information processing systems (Vol. 2017, pp. 1025\u20131035).","key":"1494_CR43"},{"doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. In 2017 IEEE international conference on computer vision (ICCV). IEEE (pp. 2980\u20132988).","key":"1494_CR44","DOI":"10.1109\/ICCV.2017.322"},{"issue":"1\u20133","key":"1494_CR45","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","volume":"17","author":"BK Horn","year":"1981","unstructured":"Horn, B. K., & Schunck, B. G. (1981). Determining optical flow. Artificial Intelligence, 17(1\u20133), 185\u2013203.","journal-title":"Artificial Intelligence"},{"issue":"6","key":"1494_CR46","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1037\/h0071325","volume":"24","author":"H Hotelling","year":"1933","unstructured":"Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417\u2013441.","journal-title":"Journal of Educational Psychology"},{"doi-asserted-by":"crossref","unstructured":"Joo, H., Simon, T., & Sheikh, Y. (2018). Total capture: A 3D deformation model for tracking faces, hands, and bodies. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition.","key":"1494_CR47","DOI":"10.1109\/CVPR.2018.00868"},{"issue":"6","key":"1494_CR48","first-page":"1681","volume":"30","author":"O van Kaick","year":"2011","unstructured":"van Kaick, O., Zhang, H., Hamarneh, G., & Cohen-Or, D. (2011). A survey on shape correspondence. Eurographics Symposium on Geometry Processing, 30(6), 1681\u20131707.","journal-title":"Eurographics Symposium on Geometry Processing"},{"key":"1494_CR49","first-page":"172","volume":"94","author":"DP Kingma","year":"2014","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. Pattern Recognition Letters, 94, 172\u2013179.","journal-title":"Pattern Recognition Letters"},{"key":"1494_CR50","first-page":"1","volume":"2017","author":"TN Kipf","year":"2017","unstructured":"Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional neural networks. ICLR, 2017, 1\u201314.","journal-title":"ICLR"},{"issue":"1","key":"1494_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-49506-1","volume":"9","author":"PG Knoops","year":"2019","unstructured":"Knoops, P. G., Papaioannou, A., Borghi, A., Breakey, R. W., Wilson, A. T., Jeelani, O., et al. (2019). A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Scientific Reports, 9(1), 1\u201312.","journal-title":"Scientific Reports"},{"doi-asserted-by":"crossref","unstructured":"Kolotouros, N., Pavlakos, G., Black, M., & Daniilidis, K. (2019a). Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In Proceedings of the IEEE international conference on computer vision (Vol. 2019, pp. 2252\u20132261).","key":"1494_CR52","DOI":"10.1109\/ICCV.2019.00234"},{"doi-asserted-by":"crossref","unstructured":"Kolotouros, N., Pavlakos, G., & Daniilidis, K. (2019b). Convolutional mesh regression for single-image human shape reconstruction. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition (Vol. 2019, pp. 4496\u20134505).","key":"1494_CR53","DOI":"10.1109\/CVPR.2019.00463"},{"key":"1494_CR54","first-page":"26","volume":"2106","author":"M Lef\u00e9bure","year":"2001","unstructured":"Lef\u00e9bure, M., & Cohen, L. D. (2001). Image registration, optical flow, and local rigidity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2106, 26\u201338.","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"doi-asserted-by":"crossref","unstructured":"Lei, H., Akhtar, N., & Mian, A. (2019). Octree guided CNN with spherical kernels for 3d point clouds. In 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 9623\u20139632).","key":"1494_CR55","DOI":"10.1109\/CVPR.2019.00986"},{"doi-asserted-by":"crossref","unstructured":"Li, G., Muller, M., Thabet, A., & Ghanem, B. (2019). DeepGCNs: Can GCNs go as deep as CNNs? In The IEEE international conference on computer vision (ICCV).","key":"1494_CR56","DOI":"10.1109\/ICCV.2019.00936"},{"unstructured":"Li, J., & Zhang, C. (2019). Iterative matching point. arXiv","key":"1494_CR57"},{"doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., & Wu, X. M. (2018). Deeper insights into graph convolutional networks for semi-supervised learning. In 32nd AAAI conference on artificial intelligence, AAAI 2018.","key":"1494_CR58","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"1494_CR59","first-page":"194-1","volume":"36","author":"T Li","year":"2017","unstructured":"Li, T., Bolkart, T., Black, M. J., Li, H., & Romero, J. (2017). Learning a model of facial shape and expression from 4D scans. ACM Transactions on Graphics, 36, 194-1.","journal-title":"ACM Transactions on Graphics"},{"doi-asserted-by":"crossref","unstructured":"Lim, I., Dielen, A., Campen, M., & Kobbelt, L. (2018). A simple approach to intrinsic correspondence learning on unstructured 3D meshes. In Computer vision\u2014ECCV 2018 workshops\u2014Munich, Germany, September 8\u201314, 2018, Proceedings, Part III (pp. 349\u2013362).","key":"1494_CR60","DOI":"10.1007\/978-3-030-11015-4_26"},{"doi-asserted-by":"crossref","unstructured":"Liu, F., Tran, L., & Liu, X. (2019). 3D face modeling from diverse raw scan data. In The IEEE international conference on computer vision (ICCV).","key":"1494_CR61","DOI":"10.1109\/ICCV.2019.00950"},{"key":"1494_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2816795.2818013","volume":"34","author":"M Loper","year":"2015","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A skinned multi-person linear model. ACM Transactions on Graphics, 34, 1\u201316.","journal-title":"ACM Transactions on Graphics"},{"doi-asserted-by":"crossref","unstructured":"Lu, W., Wan, G., Zhou, Y., Fu, X., Yuan, P., & Song, S. (2019). DeepVCP: An end-to-end deep neural network for point cloud registration. In Proceedings of the IEEE international conference on computer vision (Vol. 2019, pp. 12\u201321).","key":"1494_CR63","DOI":"10.1109\/ICCV.2019.00010"},{"unstructured":"Lucas, B. D., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In Proceedings of the 7th international joint conference on artificial intelligence (IJCAI) (Vol.\u00a02, pp. 674\u2013679).","key":"1494_CR64"},{"key":"1494_CR65","doi-asserted-by":"publisher","first-page":"1860","DOI":"10.1109\/TPAMI.2017.2739743","volume":"40","author":"M Luthi","year":"2018","unstructured":"Luthi, M., Gerig, T., Jud, C., & Vetter, T. (2018). Gaussian process morphable models. IEEE Transactions on Pattern Analysis and Machine, 40, 1860\u20131873. Intelligence.","journal-title":"IEEE Transactions on Pattern Analysis and Machine"},{"issue":"8","key":"1494_CR66","doi-asserted-by":"publisher","first-page":"1860","DOI":"10.1109\/TPAMI.2017.2739743","volume":"40","author":"M L\u00fcthi","year":"2018","unstructured":"L\u00fcthi, M., Gerig, T., Jud, C., & Vetter, T. (2018). Gaussian process morphable models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(8), 1860\u20131873.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"doi-asserted-by":"crossref","unstructured":"Masci, J., Boscaini, D., Bronstein, M. M., & Vandergheynst, P. (2015). Geodesic convolutional neural networks on Riemannian manifolds. In Proceedings of the IEEE international conference on computer vision (Vol. 2015, pp. 832\u2013840).","key":"1494_CR67","DOI":"10.1109\/ICCVW.2015.112"},{"doi-asserted-by":"crossref","unstructured":"Monti, F., Boscaini, D., Masci, J., Rodol\u00e1, E., Svoboda, J., & Bronstein, M. M. (2017). Geometric deep learning on graphs and manifolds using mixture model CNNs. In Proceedings\u201430th IEEE conference on computer vision and pattern recognition, CVPR 2017 (Vol. 2017, pp. 5425\u20135434).","key":"1494_CR68","DOI":"10.1109\/CVPR.2017.576"},{"issue":"8","key":"1494_CR69","doi-asserted-by":"publisher","first-page":"e67","DOI":"10.1016\/j.bjoms.2011.02.007","volume":"49","author":"A Mueller","year":"2011","unstructured":"Mueller, A., Paysan, P., Schumacher, R., Zeilhofer, H. F., Berg-Boerner, B. I., Maurer, J., et al. (2011). Missing facial parts computed by a morphable model and transferred directly to a polyamide laser-sintered prosthesis: An innovation study. British Journal of Oral and Maxillofacial Surgery, 49(8), e67\u2013e71.","journal-title":"British Journal of Oral and Maxillofacial Surgery"},{"key":"1494_CR70","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.1109\/TPAMI.2010.46","volume":"32","author":"A Myronenko","year":"2010","unstructured":"Myronenko, A., & Song, X. (2010). Point set registration: Coherent point drifts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 2262\u20132275.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1494_CR71","first-page":"1009","volume":"19","author":"A Myronenko","year":"2007","unstructured":"Myronenko, A., Song, X., & Carreira-Perpi\u00f1\u00e1n, M. \u00c1. (2007). Non-rigid point set registration: Coherent point drift. Advances in Neural Information Processing Systems, 19, 1009.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"6","key":"1494_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3355089.3356498","volume":"38","author":"M Nimier-David","year":"2019","unstructured":"Nimier-David, M., Vicini, D., Zeltner, T., & Jakob, W. (2019). Mitsuba 2: A retargetable forward and inverse renderer. Transactions on Graphics (Proceedings of SIGGRAPH Asia), 38(6), 1\u201317.","journal-title":"Transactions on Graphics (Proceedings of SIGGRAPH Asia)"},{"doi-asserted-by":"crossref","unstructured":"Patel, A., & Smith, W. A. P. (2009). 3D morphable face models revisited. In 2009 IEEE conference on computer vision and pattern recognition. IEEE (pp. 1327\u20131334).","key":"1494_CR73","DOI":"10.1109\/CVPRW.2009.5206522"},{"issue":"6","key":"1494_CR74","first-page":"559","volume":"2","author":"K Pearson","year":"1901","unstructured":"Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(6), 559\u2013572.","journal-title":"Philosophical Magazine"},{"unstructured":"Pharr, M., Jakob, W., & Humphreys, G. (2016). Physically based rendering: From theory to implementation (3rd ed.). Morgan Kaufmann Publishers Inc.","key":"1494_CR75"},{"doi-asserted-by":"crossref","unstructured":"Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J., & Worek, W. (2005). Overview of the face recognition grand challenge. In Proceedings\u20142005 IEEE computer society conference on computer vision and pattern recognition, CVPR 2005.","key":"1494_CR76","DOI":"10.1109\/CVPR.2005.268"},{"doi-asserted-by":"crossref","unstructured":"Ploumpis, S., Wang, H., Pears, N., Smith, W. A., & Zafeiriou, S. (2019). Combining 3D morphable models: A large scale face-and-head model. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition.","key":"1494_CR77","DOI":"10.1109\/CVPR.2019.01119"},{"key":"1494_CR78","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2991150","author":"S Ploumpis","year":"2020","unstructured":"Ploumpis, S., Ververas, E., O\u2019Sullivan, E., Moschoglou, S., Wang, H., Pears, N., et al. (2020). Towards a complete 3D morphable model of the human head. IEEE Transactions on Pattern Analysis and Machine Intelligence,. https:\/\/doi.org\/10.1109\/TPAMI.2020.2991150.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"unstructured":"Qi, C. R., Su, H., Kaichun, M., & Guibas, L. J. (2017a). PointNet: Deep learning on point sets for 3D classification and segmentation. In 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE (pp. 77\u201385).","key":"1494_CR79"},{"unstructured":"Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017b). PointNet++: Deep hierarchical feature learning on point sets in a metric space. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 30, pp. 5099\u20135108). Curran Associates Inc.","key":"1494_CR80"},{"doi-asserted-by":"crossref","unstructured":"Ranjan, A., Bolkart, T., Sanyal, S., & Black, M. J. (2018). Generating 3D faces using convolutional mesh autoencoders. In The European conference on computer vision (ECCV).","key":"1494_CR81","DOI":"10.1007\/978-3-030-01219-9_43"},{"key":"1494_CR82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3130800.3130883","volume":"36","author":"J Romero","year":"2017","unstructured":"Romero, J., Tzionas, D., & Black, M. J. (2017). Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics, 36, 1\u201317.","journal-title":"ACM Transactions on Graphics"},{"key":"1494_CR83","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1007\/s00138-013-0579-9","volume":"25","author":"A Salazar","year":"2014","unstructured":"Salazar, A., Wuhrer, S., Shu, C., & Prieto, F. (2014). Fully automatic expression-invariant face correspondence. Machine Vision and Applications, 25, 859\u2013879.","journal-title":"Machine Vision and Applications"},{"doi-asserted-by":"crossref","unstructured":"Savran, A., Aly\u00fcz, N., Dibeklio\u011flu, H., \u00c7eliktutan, O., G\u00f6kberk, B., Sankur, B., & Akarun, L. (2008). Bosphorus database for 3d face analysis. In B. Schouten, N. C. Juul, A. Drygajlo, & M. Tistarelli (Eds.), Biometrics and identity management (pp. 47\u201356). Springer.","key":"1494_CR84","DOI":"10.1007\/978-3-540-89991-4_6"},{"doi-asserted-by":"publisher","unstructured":"Shimada, S., Golyanik, V., Tretschk, E., Stricker, D., & Theobalt, C. (2019). DispVoxNets: Non-rigid point set alignment with supervised learning proxies. In Proceedings\u20142019 international conference on 3D vision, 3DV 2019. https:\/\/doi.org\/10.1109\/3DV.2019.00013.","key":"1494_CR85","DOI":"10.1109\/3DV.2019.00013"},{"doi-asserted-by":"crossref","unstructured":"Szegedy, C., Wei, L., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE (pp. 1\u20139).","key":"1494_CR86","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"7","key":"1494_CR87","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1109\/TVCG.2012.310","volume":"19","author":"GK Tam","year":"2013","unstructured":"Tam, G. K., Cheng, Z. Q., Lai, Y. K., Langbein, F. C., Liu, Y., Marshall, D., et al. (2013). Registration of 3d point clouds and meshes: A survey from rigid to nonrigid. IEEE Transactions on Visualization and Computer Graphics, 19(7), 1199\u20131217.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"doi-asserted-by":"crossref","unstructured":"Tena, J. R., De La Torre, F., & Matthews, I. (2011). Interactive region-based linear 3D face models. ACM Transactions on Graphics, 30(4), 1\u201310.","key":"1494_CR88","DOI":"10.1145\/2010324.1964971"},{"doi-asserted-by":"crossref","unstructured":"Tran, L., & Liu, X. (2018). Nonlinear 3D face morphable model. In 2018 IEEE\/CVF conference on computer vision and pattern recognition. IEEE (pp. 7346\u20137355).","key":"1494_CR89","DOI":"10.1109\/CVPR.2018.00767"},{"doi-asserted-by":"crossref","unstructured":"Tran, L., Liu, F., & Liu, X. (2019). Towards high-fidelity nonlinear 3D face morphable model. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition.","key":"1494_CR90","DOI":"10.1109\/CVPR.2019.00122"},{"unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f3, P., & Bengio, Y. (2018). Graph attention networks. In ICLR.","key":"1494_CR91"},{"doi-asserted-by":"crossref","unstructured":"Verma, N., Boyer, E., & Verbeek, J. (2018). FeaStNet: Feature-steered graph convolutions for 3D shape analysis. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition.","key":"1494_CR92","DOI":"10.1109\/CVPR.2018.00275"},{"issue":"3","key":"1494_CR93","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1145\/1073204.1073209","volume":"24","author":"D Vlasic","year":"2005","unstructured":"Vlasic, D., Brand, M., Pfister, H., & Popovi\u0107, J. (2005). Face transfer with multilinear models. ACM Transactions on Graphics, 24(3), 426\u2013433.","journal-title":"ACM Transactions on Graphics"},{"doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., & Liu, W. (2018). CosFace: Large margin cosine loss for deep face recognition. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition.","key":"1494_CR94","DOI":"10.1109\/CVPR.2018.00552"},{"doi-asserted-by":"crossref","unstructured":"Wang, Y., & Solomon, J. (2019a). Deep closest point: Learning representations for point cloud registration. In Proceedings of the IEEE international conference on computer vision.","key":"1494_CR95","DOI":"10.1109\/ICCV.2019.00362"},{"unstructured":"Wang, Y., & Solomon, J. M. (2019b). PRNet: Self-supervised learning for partial-to-partial registration. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d\u2019Alch\u00e9-Buc, E. Fox, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 32, pp. 8814\u20138826). Curran Associates Inc.","key":"1494_CR96"},{"issue":"5","key":"1494_CR97","doi-asserted-by":"publisher","first-page":"146:1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019). Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, 38(5), 146:1\u2013146:12.","journal-title":"ACM Transactions on Graphics"},{"issue":"3","key":"1494_CR98","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1007\/s11263-019-01198-w","volume":"128","author":"Y Wu","year":"2020","unstructured":"Wu, Y., & He, K. (2020). Group normalization. International Journal of Computer Vision, 128(3), 742\u2013755.","journal-title":"International Journal of Computer Vision"},{"doi-asserted-by":"crossref","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., & Qiao, Y. (2018). SpiderCNN: Deep learning on point sets with parameterized convolutional filters. In The European conference on computer vision (ECCV) 11212 LNCS (pp. 90\u2013105).","key":"1494_CR99","DOI":"10.1007\/978-3-030-01237-3_6"},{"unstructured":"Yin, L., Wei, X., Sun, Y., Wang, J., & Rosato, M. J. M. (2006). A 3D facial expression database for facial behavior research. In 7th international conference on automatic face and gesture recognition (FGR06). IEEE (pp. 211\u2013216).","key":"1494_CR100"},{"doi-asserted-by":"crossref","unstructured":"Yin, L., Chen, X., Sun, Y., Worm, T., & Reale, M. (2008). A high-resolution 3D dynamic facial expression database. In 2008 8th IEEE international conference on automatic face and gesture recognition, FG 2008. IEEE (pp. 1\u20136).","key":"1494_CR101","DOI":"10.1109\/AFGR.2008.4813324"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Z., Hua, B. S., & Yeung, S. K. (2019). Shellnet: Efficient point cloud convolutional neural networks using concentric shells statistics. In International conference on computer vision (ICCV).","key":"1494_CR102","DOI":"10.1109\/ICCV.2019.00169"},{"unstructured":"Zhu, X., Lei, Z., Yan, J., Yi, D., & Li, S. Z. (2015). High-fidelity pose and expression normalization for face recognition in the wild. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition.","key":"1494_CR103"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-021-01494-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-021-01494-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-021-01494-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T16:45:13Z","timestamp":1672764313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-021-01494-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,10]]},"references-count":103,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["1494"],"URL":"https:\/\/doi.org\/10.1007\/s11263-021-01494-4","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"type":"print","value":"0920-5691"},{"type":"electronic","value":"1573-1405"}],"subject":[],"published":{"date-parts":[[2021,7,10]]},"assertion":[{"value":"15 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"A pre-trained model will be released publicly along with code, please visit.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}