{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:16:00Z","timestamp":1776917760828,"version":"3.51.2"},"reference-count":61,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["92146001"],"award-info":[{"award-number":["92146001"]}]},{"name":"National Natural Science Foundation of China","award":["61673151"],"award-info":[{"award-number":["61673151"]}]},{"name":"National Natural Science Foundation of China","award":["19ZDA324"],"award-info":[{"award-number":["19ZDA324"]}]},{"name":"National Natural Science Foundation of China","award":["2020QDL005"],"award-info":[{"award-number":["2020QDL005"]}]},{"name":"Major Project of The National Social Science Fund of China","award":["92146001"],"award-info":[{"award-number":["92146001"]}]},{"name":"Major Project of The National Social Science Fund of China","award":["61673151"],"award-info":[{"award-number":["61673151"]}]},{"name":"Major Project of The National Social Science Fund of China","award":["19ZDA324"],"award-info":[{"award-number":["19ZDA324"]}]},{"name":"Major Project of The National Social Science Fund of China","award":["2020QDL005"],"award-info":[{"award-number":["2020QDL005"]}]},{"name":"Scientific Research Foundation for Scholars of HZNU","award":["92146001"],"award-info":[{"award-number":["92146001"]}]},{"name":"Scientific Research Foundation for Scholars of HZNU","award":["61673151"],"award-info":[{"award-number":["61673151"]}]},{"name":"Scientific Research Foundation for Scholars of HZNU","award":["19ZDA324"],"award-info":[{"award-number":["19ZDA324"]}]},{"name":"Scientific Research Foundation for Scholars of HZNU","award":["2020QDL005"],"award-info":[{"award-number":["2020QDL005"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["92146001"],"award-info":[{"award-number":["92146001"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["61673151"],"award-info":[{"award-number":["61673151"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["19ZDA324"],"award-info":[{"award-number":["19ZDA324"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2020QDL005"],"award-info":[{"award-number":["2020QDL005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We present TED-Face, a new method for recovering high-fidelity 3D facial geometry and appearance with enhanced textures from single-view images. While vision-based face reconstruction has received intensive research in the past decades due to its broad applications, it remains a challenging problem because human eyes are particularly sensitive to numerically minute yet perceptually significant details. Previous methods that seek to minimize reconstruction errors within a low-dimensional face space can suffer from this issue and generate close yet low-fidelity approximations. The loss of high-frequency texture details is a key factor in their process, which we propose to address by learning to recover both dense radiance residuals and sparse facial texture features from a single image, in addition to the variables solved by previous work\u2014shape, appearance, illumination, and camera. We integrate the estimation of all these factors in a single unified deep neural network and train it on several popular face reconstruction datasets. We also introduce two new metrics, visual fidelity (VIF) and structural similarity (SSIM), to compensate for the fact that reconstruction error is not a consistent perceptual metric of quality. On the popular FaceWarehouse facial reconstruction benchmark, our proposed system achieves a VIF score of 0.4802 and an SSIM score of 0.9622, improving over the state-of-the-art Deep3D method by 6.69% and 0.86%, respectively. On the widely used LS3D-300W dataset, we obtain a VIF score of 0.3922 and an SSIM score of 0.9079 for indoor images, and the scores for outdoor images are 0.4100 and 0.9160, respectively, which also represent an improvement over those of Deep3D. These results show that our method is able to recover visually more realistic facial appearance details compared with previous methods.<\/jats:p>","DOI":"10.3390\/s23146525","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T21:22:58Z","timestamp":1689801778000},"page":"6525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["TED-Face: Texture-Enhanced Deep Face Reconstruction in the Wild"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6239-192X","authenticated-orcid":false,"given":"Ying","family":"Huang","sequence":"first","affiliation":[{"name":"Institute of Virtual Reality and Intelligent Systems, Hangzhou Normal University, Hangzhou 311121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Fang","sequence":"additional","affiliation":[{"name":"Institute of Virtual Reality and Intelligent Systems, Hangzhou Normal University, Hangzhou 311121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0709-8384","authenticated-orcid":false,"given":"Shanfeng","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4028","DOI":"10.1109\/TMM.2021.3111485","article-title":"Prior-Guided Multi-View 3D Head Reconstruction","volume":"24","author":"Wang","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1068\/p7462","article-title":"Holistic face processing is induced by shape and texture","volume":"42","author":"Persike","year":"2013","journal-title":"Perception"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Blanz, V., and Vetter, T. (1999, January 8\u201313). A morphable model for the synthesis of 3D faces. Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA, USA.","DOI":"10.1145\/311535.311556"},{"key":"ref_4","unstructured":"Gecer, B., Lattas, A., Ploumpis, S., Deng, J., Papaioannou, A., Moschoglou, S., and Zafeiriou, S. (2020). European Conference on Computer Vision, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gecer, B., Ploumpis, S., Kotsia, I., and Zafeiriou, S. (2019, January 15\u201320). Ganfit: Generative adversarial network fitting for high fidelity 3d face reconstruction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00125"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tewari, A., Zollh\u00f6fer, M., Garrido, P., Bernard, F., Kim, H., P\u00e9rez, P., and Theobalt, C. (2018, January 18\u201323). Self-supervised multi-level face model learning for monocular reconstruction at over 250 hz. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00270"},{"key":"ref_7","first-page":"157","article-title":"On learning 3d face morphable model from in-the-wild images","volume":"43","author":"Tran","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhu, W., Wu, H., Chen, Z., Vesdapunt, N., and Wang, B. (2020, January 13\u201319). Reda: Reinforced differentiable attribute for 3d face reconstruction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00501"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tewari, A., Zollhofer, M., Kim, H., Garrido, P., Bernard, F., Perez, P., and Theobalt, C. (2017, January 22\u201329). Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.153"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3169","DOI":"10.1109\/TMM.2021.3094300","article-title":"Towards Analysis-Friendly Face Representation With Scalable Feature and Texture Compression","volume":"24","author":"Wang","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_11","unstructured":"Havin, V., and J\u00f6ricke, B. (2012). The Uncertainty Principle in Harmonic Analysis, Springer Science & Business Media."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Soler, C., Molazem, R., and Subr, K. (, January 7\u201311). A Theoretical Analysis of Compactness of the Light Transport Operator. Proceedings of the ACM SIGGRAPH 2022 Conference Proceedings, Vancouver, BC, Canada.","DOI":"10.1145\/3528233.3530725"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Deng, J., Cheng, S., Xue, N., Zhou, Y., and Zafeiriou, S. (2018, January 18\u201323). Uv-gan: Adversarial facial uv map completion for pose-invariant face recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00741"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lin, J., Yuan, Y., Shao, T., and Zhou, K. (2020, January 13\u201319). Towards high-fidelity 3D face reconstruction from in-the-wild images using graph convolutional networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00593"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, J., Yang, J., and Tong, X. (2021, January 10\u201317). Learning High-Fidelity Face Texture Completion without Complete Face Texture. Proceedings of the IEEE\/CVF International Conference on Computer, Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01373"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lattas, A., Moschoglou, S., Gecer, B., Ploumpis, S., Triantafyllou, V., Ghosh, A., and Zafeiriou, S. (2020, January 13\u201319). AvatarMe: Realistically Renderable 3D Facial Reconstruction \u201cin-the-wild\u201d. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00084"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Deng, Y., Yang, J., Xu, S., Chen, D., Jia, Y., and Tong, X. (2019, January 15\u201320). Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00038"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, P., Gangnet, M., and Blake, A. (2003, January 27\u201331). Poisson image editing. Proceedings of the SIGGRAPH 2003, Special Interest Group on Computer Graphics and Interactive Techniques, San Diego, CA, USA.","DOI":"10.1145\/1201775.882269"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., and Tang, X. (2015, January 7\u201313). Deep learning face attributes in the wild. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lei, Z., Liu, X., Shi, H., and Li, S.Z. (2016, January 27\u201330). Face alignment across large poses: A 3d solution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.23"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Klare, B.F., Klein, B., Taborsky, E., Blanton, A., Cheney, J., Allen, K., Grother, P., Mah, A., and Jain, A.K. (2015, January 7\u201312). Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298803"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_24","unstructured":"Huang, G.B., Mattar, M., Berg, T., and Learned-Miller, E. (2008). Workshop on Faces in\u2019Real-Life\u2019Images: Detection, Alignment, and Recognition, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bulat, A., and Tzimiropoulos, G. (2017, January 22\u201329). How far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks). Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.116"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TIP.2005.859378","article-title":"Image information and visual quality","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","unstructured":"Moccozet, L., and Thalmann, N.M. (1997). Dirichlet Free-Form Deformations and Their Application to Hand Simulation, IEEE."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"DeCarlo, D., Metaxas, D., and Stone, M. (1998, January 19\u201324). An anthropometric face model using variational techniques. Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, Orlando, FL, USA.","DOI":"10.1145\/280814.280823"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1126\/science.272.5270.1905","article-title":"Image representations for visual learning","volume":"272","author":"Beymer","year":"1996","journal-title":"Science"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Choi, C.S., Okazaki, T., Harashima, H., and Takebe, T. (1991, January 11\u201314). A system of analyzing and synthesizing facial images. Proceedings of the 1991 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore.","DOI":"10.1109\/ISCAS.1991.176094"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/34.598231","article-title":"Automatic interpretation and coding of face images using flexible models","volume":"19","author":"Lanitis","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Paysan, P., Knothe, R., Amberg, B., Romdhani, S., and Vetter, T. (2009, January 2\u20134). A 3D face model for pose and illumination invariant face recognition. Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, Genova, Italy.","DOI":"10.1109\/AVSS.2009.58"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tran, L., and Liu, X. (2018, January 18\u201323). Nonlinear 3d face morphable model. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00767"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, Y., Hu, S., and Zhang, Z. (2022, January 21\u201324). Structured Spatial Reasoning for Human Pose Estimation. Proceedings of the 33rd British Machine Vision Conference, London, UK.","DOI":"10.1007\/s00138-022-01334-6"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103010","DOI":"10.1016\/j.cviu.2020.103010","article-title":"High-speed multi-person pose estimation with deep feature transfer","volume":"197\u2013198","author":"Huang","year":"2020","journal-title":"Computer Vision and Image Understanding."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhuang, J., and Qin, Z. (2019, January 22\u201325). Multi-Level Network for High-Speed Multi-Person Pose Estimation. Proceedings of the 2019 IEEE International Conference on Image Processing, Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8804198"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Huang, Y., Sun, B., Kan, H., Zhuang, J., and Qin, Z. (2019, January 8\u201311). FollowMeUp Sports: New Benchmark for 2D Human Keypoint Recognition. Proceedings of the Pattern Recognition and Computer Vision\u2014Second Chinese Conference, Xi\u2019an, China.","DOI":"10.1007\/978-3-030-31726-3_10"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zheng, M., Wang, F., You, S., Qian, C., Zhang, C., Wang, X., and Xu, C. (2021, January 10\u201317). Weakly supervised contrastive learning. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00989"},{"key":"ref_41","unstructured":"Joulin, A., Maaten, L.v.d., Jabri, A., and Vasilache, N. (2016). European Conference on Computer Vision, Springer."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Saito, S., Yang, J., Ma, Q., and Black, M.J. (2021, January 20\u201325). SCANimate: Weakly supervised learning of skinned clothed avatar networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00291"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1109\/JSTSP.2018.2877108","article-title":"Multi-attribute robust component analysis for facial uv maps","volume":"12","author":"Moschoglou","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1111\/1467-8659.t01-1-00712","article-title":"Reanimating faces in images and video","volume":"Volume 22","author":"Blanz","year":"2003","journal-title":"Computer Graphics Forum"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hong, Y., Peng, B., Xiao, H., Liu, L., and Zhang, J. (2022, January 18\u201324). Headnerf: A real-time nerf-based parametric head model. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01973"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"B\u00fchler, M.C., Meka, A., Li, G., Beeler, T., and Hilliges, O. (2021, January 10\u201317). VariTex: Variational Neural Face Textures. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01363"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Deng, Y., Yang, J., Chen, D., Wen, F., and Tong, X. (2020, January 13\u201319). Disentangled and controllable face image generation via 3d imitative-contrastive learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00520"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ghosh, P., Gupta, P.S., Uziel, R., Ranjan, A., Black, M.J., and Bolkart, T. (2020, January 25\u201328). GIF: Generative interpretable faces. Proceedings of the 2020 International Conference on 3D Vision (3DV), Fukuoka, Japan.","DOI":"10.1109\/3DV50981.2020.00097"},{"key":"ref_49","first-page":"413","article-title":"Facewarehouse: A 3d facial expression database for visual computing","volume":"20","author":"Cao","year":"2013","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ramamoorthi, R., and Hanrahan, P. (2001, January 12\u201317). An efficient representation for irradiance environment maps. Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA, USA.","DOI":"10.1145\/383259.383317"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ramamoorthi, R., and Hanrahan, P. (2001, January 12\u201317). A signal-processing framework for inverse rendering. Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA, USA.","DOI":"10.1145\/383259.383271"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., and Nie\u00dfner, M. (2016, January 27\u201330). Face2face: Real-time face capture and reenactment of rgb videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.262"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_54","unstructured":"Kingma, D.P., and Ba, J. (2015). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s00138-022-01349-z","article-title":"Fast re-OBJ: Real-time object re-identification in rigid scenes","volume":"33","author":"Bayraktar","year":"2022","journal-title":"Mach. Vis. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"8696","DOI":"10.1109\/TIP.2020.3017347","article-title":"Self-supervised learning of detailed 3d face reconstruction","volume":"29","author":"Chen","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_58","unstructured":"Shang, J., Shen, T., Li, S., Zhou, L., Zhen, M., Fang, T., and Quan, L. (2020). European Conference on Computer Vision, Springer."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ju, Y.J., Lee, G.H., Hong, J.H., and Lee, S.W. (2022, January 3\u20138). Complete face recovery gan: Unsupervised joint face rotation and de-occlusion from a single-view image. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00124"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Booth, J., Antonakos, E., Ploumpis, S., Trigeorgis, G., Panagakis, Y., and Zafeiriou, S. (2017, January 21\u201326). 3d face morphable models\" in-the-wild\". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.580"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Feng, Y., Wu, F., Shao, X., Wang, Y., and Zhou, X. (2018, January 8\u201314). Joint 3d face reconstruction and dense alignment with position map regression network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_33"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6525\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:14:59Z","timestamp":1760127299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,19]]},"references-count":61,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23146525"],"URL":"https:\/\/doi.org\/10.3390\/s23146525","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,19]]}}}