{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T21:44:44Z","timestamp":1773697484123,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>High-fidelity 3D human body reconstruction is challenging, as single-view methods often lead to distortions due to self-occlusion, and the existing multi-view approaches either focus on pose or exhibit limited performance. This study presents an efficient approach to realistic 3D human body reconstruction from front and back images, emphasizing symmetry and surface detail preservation. We begin by extracting the key points and pose information from dual-view images, applying SMPL-X to generate an initial 3D body. Then, using normal maps derived from both views, we infer high-fidelity surfaces and optimize SMPL-X based on these reconstructed surfaces. Through implicit modeling, we merge the front and back surfaces, ensuring a symmetric fusion boundary for a complete 3D body model. Our experimental results on the THuman2.0 dataset demonstrate our method\u2019s effectiveness, with significant improvements in the surface detail fidelity. To validate the model\u2019s accuracy further, we collected waist and chest circumference measurements from 120 individuals, finding an average measurement error below 0.8 centimeters, thus confirming the robustness of SMPL-X optimized with dual-view data.<\/jats:p>","DOI":"10.3390\/sym16121647","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T08:36:32Z","timestamp":1733992592000},"page":"1647","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Three-Dimensional Human Body Reconstruction Using Dual-View Normal Maps"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0787-5806","authenticated-orcid":false,"given":"Yukun","family":"Dong","sequence":"first","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Shengtao","family":"Wang","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Junqi","family":"Sun","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Menghua","family":"Wang","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Long","family":"Cheng","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yi, H., Huang, C.H.P., Tripathi, S., Hering, L., Thies, J., and Black, M.J. (2023, January 17\u201324). MIME: Human-aware 3D scene generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01246"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yi, H., Liang, H., Liu, Y., Cao, Q., Wen, Y., Bolkart, T., Tao, D., and Black, M.J. (2023, January 17\u201324). Generating holistic 3D human motion from speech. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00053"},{"key":"ref_3","unstructured":"Bhatnagar, B.L., Tiwari, G., Theobalt, C., and Pons-Moll, G. (November, January 27). Multi-garment net: Learning to dress 3D people from images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1111\/cgf.13643","article-title":"Learning-based animation of clothing for virtual try-on","volume":"38","author":"Santesteban","year":"2019","journal-title":"Comput. Graph. Forum"},{"key":"ref_5","unstructured":"Kolotouros, N., Pavlakos, G., Black, M.J., and Daniilidis, K. (November, January 27). Learning to reconstruct 3D human pose and shape via model-fitting in the loop. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A.A., Tzionas, D., and Black, M.J. (2019, January 15\u201320). Expressive body capture: 3D hands, face, and body from a single image. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01123"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chun, S., Park, S., and Chang, J.Y. (2023, January 2\u20137). Learnable human mesh triangulation for 3D human pose and shape estimation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV56688.2023.00287"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sayo, A., Thomas, D., Kawasaki, H., Nakashima, Y., and Ikeuchi, K. (2021, January 19\u201322). PoseRN: A 2D pose refinement network for bias-free multi-view 3D human pose estimation. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506022"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Z., Heyden, A., and Oskarsson, M. (2021, January 10\u201315). A novel joint points and silhouette-based method to estimate 3D human pose and shape. Proceedings of the Pattern Recognition\u2014ICPR International Workshops and Challenges, Virtual. Proceedings, Part I.","DOI":"10.1007\/978-3-030-68763-2_4"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Shao, R., Zhang, Y., Yu, T., Zheng, Z., Dai, Q., and Liu, Y. (2021, January 10\u201317). Deepmulticap: Performance capture of multiple characters using sparse multiview cameras. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00618"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, J., Yi, W., Wang, T., Li, X., Ma, L., Fan, Y., and Lu, H. (2022). Pixel2ISDF: Implicit Signed Distance Fields Based Human Body Model from Multi-view and Multi-pose Images. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-031-25072-9_24"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1145\/3503250","article-title":"Nerf: Representing scenes as neural radiance fields for view synthesis","volume":"65","author":"Mildenhall","year":"2021","journal-title":"Commun. ACM"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Suo, X., Jiang, Y., Lin, P., Zhang, Y., Wu, M., Guo, K., and Xu, L. (2021, January 20\u201325). Neuralhumanfvv: Real-time neural volumetric human performance rendering using rgb cameras. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00616"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, F., Yang, W., Zhang, J., Lin, P., Zhang, Y., Yu, J., and Xu, L. (2022, January 18\u201324). Humannerf: Efficiently generated human radiance field from sparse inputs. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00759"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Song, T., Zhang, R., Dong, Y., Liu, F., Zhang, Y., and Peng, R. (2021, January 9\u201312). MMDA: Disease analysis model based on anthropometric measurement. Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, TX, USA.","DOI":"10.1109\/BIBM52615.2021.9669310"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s10489-023-05214-y","article-title":"An iterative 3D human body reconstruction method driven by personalized dimensional prior knowledge","volume":"54","author":"Dong","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_17","first-page":"851","article-title":"SMPL: A skinned multi-person linear model","volume":"Volume 2","author":"Loper","year":"2023","journal-title":"Seminal Graphics Papers: Pushing the Boundaries"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Correia, H.A., and Brito, J.H. (2023). 3D reconstruction of human bodies from single-view and multi-view images: A systematic review. Comput. Methods Programs Biomed., 239.","DOI":"10.1016\/j.cmpb.2023.107620"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Weng, C.Y., Curless, B., Srinivasan, P.P., Barron, J.T., and Kemelmacher-Shlizerman, I. (2022, January 18\u201324). Humannerf: Free-viewpoint rendering of moving people from monocular video. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01573"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alldieck, T., Magnor, M., Xu, W., Theobalt, C., and Pons-Moll, G. (2018, January 18\u201323). Video based reconstruction of 3D people models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00875"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, R., Xiu, Y., Saito, S., Huang, Z., Olszewski, K., and Li, H. (2020, January 23\u201328). Monocular real-time volumetric performance capture. Proceedings of the Computer Vision\u2014ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part XXIII 16.","DOI":"10.1007\/978-3-030-58592-1_4"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xiu, Y., Yang, J., Cao, X., Tzionas, D., and Black, M.J. (2023, January 17\u201324). Econ: Explicit clothed humans optimized via normal integration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00057"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xiu, Y., Yang, J., Tzionas, D., and Black, M.J. (2022, January 18\u201324). Icon: Implicit clothed humans obtained from normals. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01294"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chibane, J., Alldieck, T., and Pons-Moll, G. (2020, January 13\u201319). Implicit functions in feature space for 3D shape reconstruction and completion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00700"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., and Lovegrove, S. (2019, January 15\u201320). Deepsdf: Learning continuous signed distance functions for shape representation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00025"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2487228.2487237","article-title":"Screened poisson surface reconstruction","volume":"32","author":"Kazhdan","year":"2013","journal-title":"ACM Trans. Graph. ToG"},{"key":"ref_27","unstructured":"Newell, A., Yang, K., and Deng, J. (2016, January 11\u201314). Stacked hourglass networks for human pose estimation. Proceedings of the Computer Vision\u2014ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part VIII 14."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Habermann, M., Xu, W., Zollhofer, M., Pons-Moll, G., and Theobalt, C. (2020, January 13\u201319). Deepcap: Monocular human performance capture using weak supervision. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00510"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lorensen, W.E., and Cline, H.E. (1998). Marching cubes: A high resolution 3D surface construction algorithm. Seminal Graphics: Pioneering Efforts That Shaped the Field, ACM.","DOI":"10.1145\/280811.281026"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yu, T., Zheng, Z., Guo, K., Liu, P., Dai, Q., and Liu, Y. (2021, January 20\u201325). Function4d: Real-time human volumetric capture from very sparse consumer rgbd sensors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00569"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1109\/TPAMI.2022.3168569","article-title":"Deepcloth: Neural garment representation for shape and style editing","volume":"45","author":"Su","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Saito, S., Simon, T., Saragih, J., and Joo, H. (2020, January 13\u201319). Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3D human digitization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00016"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, X., Luo, Y., Xiu, Y., Wang, W., Xu, H., and Fan, Z. (2023, January 1\u20136). D-if: Uncertainty-aware human digitization via implicit distribution field. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00837"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tang, J., Zhou, H., Chen, X., Hu, T., Ding, E., Wang, J., and Zeng, G. (2023, January 1\u20136). Delicate textured mesh recovery from nerf via adaptive surface refinement. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01626"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tancik, M., Weber, E., Ng, E., Li, R., Yi, B., Wang, T., Kristoffersen, A., Austin, J., Salahi, K., and Ahuja, A. (2023). Nerfstudio: A modular framework for neural radiance field development. Proceedings of the ACM SIGGRAPH 2023 Conference Proceedings, ACM.","DOI":"10.1145\/3588432.3591516"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/12\/1647\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T09:14:44Z","timestamp":1760433284000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/12\/1647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,12]]},"references-count":35,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["sym16121647"],"URL":"https:\/\/doi.org\/10.3390\/sym16121647","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,12]]}}}