{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:15:06Z","timestamp":1788952506288,"version":"build-2803163510"},"reference-count":105,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Human hands play a central role in manipulation, communication, and physical interaction, making their accurate digital reconstruction a long-standing challenge in computer vision and graphics. Reliable modeling of hand pose, hand shape, and interaction is essential for applications ranging from immersive virtual and augmented reality to robotics, activity understanding, and human\u2013machine interfaces. Over the past decade, research has evolved from isolated single-hand pose estimation toward increasingly holistic frameworks that jointly reconstruct two hands and the objects they manipulate. This review provides a comprehensive overview of hand reconstruction methods, tracing the progression from classical model-based approaches and early learning-driven pipelines to modern systems capable of two-hand interaction modeling and category-agnostic hand\u2013object reconstruction. We structure the surveyed literature according to fundamental algorithmic paradigms, encompassing model-based formulations, convolutional and graph-based learning methods, transformer-based architectures, and emerging neural implicit and Gaussian representations. This review is tailored for researchers new to the field and follows a chronological and conceptual organization that elucidates how key design choices have shaped current capabilities and limitations. We analyze persistent challenges that hinder the widespread adoption of hand reconstruction methods in practical applications, including articulation complexity, generalization to unseen objects, and computational efficiency. Finally, this paper provides a forward-looking perspective on emerging research directions, highlighting trends toward real-time, category-agnostic, and semantically meaningful hand reconstruction systems for future human-centered computing.<\/jats:p>","DOI":"10.3390\/jimaging12090425","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:19:16Z","timestamp":1788949156000},"page":"425","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Review of Hand Reconstruction Methods: From Hand Pose to Neural Reconstruction of Hands with Category-Agnostic Objects"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5919-7202","authenticated-orcid":false,"given":"Ahmed","family":"Elhayek","sequence":"first","affiliation":[{"name":"Artificial Intelligence Department, University of Prince Mugrin, Madinah 42381, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.robot.2008.10.024","article-title":"A survey of robot learning from demonstration","volume":"57","author":"Argall","year":"2009","journal-title":"Robot. Auton. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2629500","article-title":"Real-time continuous pose recovery of human hands using convolutional networks","volume":"33","author":"Tompson","year":"2014","journal-title":"ACM Trans. Graph."},{"key":"ref_3","unstructured":"Oberweger, M., Wohlhart, P., and Lepetit, V. (2015). Hands deep in deep learning for hand pose estimation. Proceedings of the 20th Computer Vision Winter Workshop, Verlag der Technischen Universit\u00e4t Graz."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Moon, G., Shiratori, T., and Lee, K.M. (2020). DeepHandMesh: A Weakly-supervised Deep Encoder-Decoder Framework for High-fidelity Hand Mesh Modeling. Proceedings of the Computer Vision\u2014ECCV 2020: 16th European Conference, Part II, Glasgow, UK, 23\u201328 August 2020, Springer.","DOI":"10.1007\/978-3-030-58536-5_26"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Baek, S., Kim, K.I., and Kim, T.K. (2020). Weakly-Supervised Domain Adaptation via GAN and Mesh Model for Estimating 3D Hand Poses Interacting Objects. Proceedings of the Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.00616"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3322958","article-title":"Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera","volume":"38","author":"Mueller","year":"2019","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hasson, Y., Tekin, B., Bogo, F., Laptev, I., Pollefeys, M., and Schmid, C. (2020). Leveraging photometric consistency over time for sparsely supervised hand\u2013object reconstruction. Proceedings of the CVPR, IEEE.","DOI":"10.1109\/CVPR42600.2020.00065"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"On, J., Gwak, K., Kang, G., Cha, J., Hwang, S., Hwang, H., and Baek, S. (2025). BIGS: Bimanual Category-Agnostic Interaction Reconstruction from Monocular Videos via 3D Gaussian Splatting. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.01625"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fan, Z., Parelli, M., Kadoglou, M.E., Kocabas, M., Chen, X., Black, M.J., and Hilliges, O. (2024). HOLD: Category-Agnostic 3D Reconstruction of Interacting Hands and Objects from Video. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00054"},{"key":"ref_10","unstructured":"Zimmermann, C., and Brox, T. (2017). Learning a model-based pose prior for 3D human pose estimation. Proceedings of the ICCV, IEEE."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Keskin, C., Kira\u00e7, F., Kara, Y.E., and Akarun, L. (2013). Hand pose estimation and hand shape classification using multi-layered randomized decision forests. Proceedings of the ECCV, IEEE.","DOI":"10.1007\/978-3-642-33783-3_61"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ge, L., Liang, H., Yuan, J., and Thalmann, D. (2018, January 18\u201323). Hand pointnet: 3D hand pose estimation using point sets. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00878"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chao, Y.W., Yang, W., Xiang, Y., Molchanov, P., Handa, A., Tremblay, J., Narang, Y.S., Van Wyk, K., Iqbal, U., and Birchfield, S. (2021, January 20\u201325). DexYCB: A Benchmark for Capturing Hand Grasping of Objects. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00893"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kwon, T., Cho, Y., Kim, J., and Lee, J. (2021). H2O: Two Hands Manipulating Objects. Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00998"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.patcog.2019.04.026","article-title":"A survey on 3D hand pose estimation: Cameras, methods, and datasets","volume":"93","author":"Li","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Farjadi, S.A., Akbarzadeh-T, M.-R., and Ghiasi-Shirazi, K. (2024). RGB Image-Based Hand Pose Estimation: A Survey on Deep Learning Perspective. Proceedings of the 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), IEEE.","DOI":"10.1109\/AISP61396.2024.10475217"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1007\/s11263-018-1081-7","article-title":"Depth-Based Hand Pose Estimation: Methods, Data, and Challenges","volume":"126","author":"Rogez","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aboukhadra, A.T., Malik, J., Elhayek, A., Robertini, N., and Stricker, D. (2023). THOR-Net: End-to-End Graformer-Based Realistic Two Hands and Object Reconstruction With Self-Supervision. Proceedings of the WACV, IEEE.","DOI":"10.1109\/WACV56688.2023.00106"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"38121","DOI":"10.1007\/s11042-025-20704-6","article-title":"Deep Learning Based on Hand Pose Estimation Methods: A Systematic Literature Review","volume":"84","author":"Bekiri","year":"2025","journal-title":"Multimed. Tools Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yuan, S., Garcia-Hernando, G., Stenger, B., Moon, G., Chang, J., Lee, K.M., Molchanov, P., Kautz, J., Honari, S., and Ge, L. (2017). 3D Hand Pose Estimation: From Current Achievements to Future Goals. arXiv.","DOI":"10.1109\/CVPR.2018.00279"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, W., Yu, C., Tu, C., Lyu, Z., Tang, J., Ou, S., Fu, Y., and Xue, Z. (2020). A Survey on Hand Pose Estimation with Wearable Sensors and Computer-Vision-Based Methods. Sensors, 20.","DOI":"10.3390\/s20041074"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.jvcir.2015.06.013","article-title":"A survey of human pose estimation: The body parts parsing based methods","volume":"32","author":"Liu","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3603618","article-title":"Deep Learning-based Human Pose Estimation: A Survey","volume":"56","author":"Zheng","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3130800.3130883","article-title":"Embodied hands: Modeling and capturing hands and bodies together","volume":"36","author":"Romero","year":"2017","journal-title":"ACM Trans. Graph."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Malik, J., Abdelaziz, I., Elhayek, A., Shimada, S., Ali, S.A., Golyanik, V., Theobalt, C., and Stricker, D. (2020, January 13\u201319). HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Map. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00714"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Malik, J., Elhayek, A., Nunnari, F., Varanasi, K., Tamaddon, K., Heloir, A., and Stricker, D. (2018). DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth. Proceedings of the International Conference on 3D Vision (3DV), IEEE.","DOI":"10.1109\/3DV.2018.00023"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Doosti, B., Naha, S., Mirbagheri, M., and Crandall, D.J. (2020). Hope-net: A graph-based model for hand-object pose estimation. Proceedings of the Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.00664"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sridhar, S., Rhodin, H., Seidel, H.P., Oulasvirta, A., and Theobalt, C. (2015). Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model. Proceedings of the 2014 International Conference on 3D Vision, 3DV 2014, IEEE.","DOI":"10.1109\/3DV.2014.37"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dibra, E., Jain, H., \u00d6ztireli, C., Ziegler, R., and Gross, M. (2017). Human Shape from Silhouettes Using Generative HKS Descriptors and Cross-Modal Neural Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2017.584"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"K\u0131ra\u00e7, F., Kara, Y., Keskin, C., and Akarun, L. (2012). Depth image based 3D hand pose estimation framework. 2012 IEEE Signal Processing and Communications Applications Conference (SIU), IEEE.","DOI":"10.1109\/SIU.2012.6204850"},{"key":"ref_31","first-page":"3","article-title":"Efficient model-based 3D tracking of hand articulations using Kinect","volume":"Volume 1","author":"Oikonomidis","year":"2011","journal-title":"Proceedings of the BmVC"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Qian, C., Sun, X., Wei, Y., Tang, X., and Sun, J. (2014). Realtime and robust hand tracking from depth. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2014.145"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1111\/cgf.12700","article-title":"Robust Articulated-ICP for Real-Time Hand Tracking","volume":"Volume 34","author":"Tagliasacchi","year":"2015","journal-title":"Proceedings of the Computer Graphics Forum"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yuan, S., Ye, Q., Stenger, B., Jain, S., and Kim, T.K. (2017). Bighand2.2m benchmark: Hand pose dataset and state of the art analysis. Proceedings of the Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2017.279"},{"key":"ref_35","unstructured":"Zhou, X., Wan, Q., Zhang, W., Xue, X., and Wei, Y. (2016). Model-based deep hand pose estimation. IJCAI\u201916: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, AAAI Press."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Malik, J., Elhayek, A., and Stricker, D. (2017). Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image. Proceedings of the International Conference on 3D Vision (3DV), IEEE.","DOI":"10.1109\/3DV.2017.00069"},{"key":"ref_37","unstructured":"Moon, G., Yong Chang, J., and Mu Lee, K. (2018). V2v-posenet: Voxel-to-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map. Proceedings of the Computer Vision and Pattern Recognition (CVPR), IEEE."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kwon, T., Tekin, B., St\u00fchmer, J., Bogo, F., and Pollefeys, M. (2021). H2O: Two Hands Manipulating Objects for First Person Interaction Recognition. Proceedings of the International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00998"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hampali, S., Rad, M., Oberweger, M., and Lepetit, V. (2020). HOnnotate: A Method for 3D Annotation of Hand and Object Poses. Proceedings of the CVPR, IEEE.","DOI":"10.1109\/CVPR42600.2020.00326"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Taheri, O., Ghorbani, N., Black, M.J., and Tzionas, D. (2020). GRAB: A Dataset of Whole-Body Human Grasping of Objects. Proceedings of the European Conference on Computer Vision (ECCV), IEEE.","DOI":"10.1007\/978-3-030-58548-8_34"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Brahmbhatt, S., Tang, C., Twigg, C.D., Kemp, C.C., and Hays, J. (2020). ContactPose: A Dataset of Grasps with Object Contact and Hand Pose. Proceedings of the European Conference on Computer Vision (ECCV), IEEE.","DOI":"10.1007\/978-3-030-58601-0_22"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Brahmbhatt, S., Ham, C., Kemp, C.C., and Hays, J. (2019). ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging. Proceedings of the Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00891"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kulon, D., Guler, R.A., Kokkinos, I., Bronstein, M.M., and Zafeiriou, S. (2020). Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild. Proceedings of the Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.00504"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ge, L., Ren, Z., Li, Y., Xue, Z., Wang, Y., Cai, J., and Yuan, J. (2019). 3D Hand Shape and Pose Estimation from a Single RGB Image. Proceedings of the Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.01109"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8962","DOI":"10.1109\/TPAMI.2021.3122874","article-title":"HandVoxNet++: 3D Hand Shape and Pose Estimation Using Voxel-Based Neural Networks","volume":"44","author":"Malik","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Dibra, E., Wolf, T., Oztireli, C., and Gross, M. (2017). How to Refine 3D Hand Pose Estimation from Unlabelled Depth Data?. 3DV, IEEE.","DOI":"10.1109\/3DV.2017.00025"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tekin, B., Bogo, F., and Pollefeys, M. (2019). H+ o: Unified egocentric recognition of 3d hand-object poses and interactions. Proceedings of the Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00464"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hasson, Y., Varol, G., Tzionas, D., Kalevatykh, I., Black, M.J., Laptev, I., and Schmid, C. (2019). Learning joint reconstruction of hands and manipulated objects. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.01208"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cao, Z., Radosavovic, I., Kanazawa, A., and Malik, J. (2021). Reconstructing hand-object interactions in the wild. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.01219"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sridhar, S., Mueller, F., Zollh\u00f6fer, M., Casas, D., Oulasvirta, A., and Theobalt, C. (2016). Real-time joint tracking of a hand manipulating an object from rgb-d input. Proceedings of the European Conference on Computer Vision (ECCV), IEEE.","DOI":"10.1007\/978-3-319-46475-6_19"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1007\/s11263-016-0895-4","article-title":"Capturing Hands in Action Using Discriminative Salient Points and Physics Simulation","volume":"118","author":"Tzionas","year":"2016","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Tsoli, A., and Argyros, A.A. (2018). Joint 3D Tracking of a Deformable Object in Interaction with a Hand. Proceedings of the European Conference on Computer Vision (ECCV), IEEE.","DOI":"10.1007\/978-3-030-01264-9_30"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, H., Zhang, Y., Shao, R., Yu, T., and Liu, Y. (2024). Hhmr: Holistic hand mesh recovery by enhancing the multimodal controllability of graph diffusion models. Proceedings of the CVPR, IEEE.","DOI":"10.1109\/CVPR52733.2024.00068"},{"key":"ref_54","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 2017, NIPS\u201917, IEEE."},{"key":"ref_55","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Park, J., Oh, Y., Moon, G., Choi, H., and Lee, K.M. (2022). Handoccnet: Occlusion-robust 3d hand mesh estimation network. Proceedings of the CVPR, IEEE.","DOI":"10.1109\/CVPR52688.2022.00155"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Shan, D., Radosavovic, I., Kanazawa, A., Fouhey, D., and Malik, J. (2024). Reconstructing Hands in 3D with Transformers. Proceedings of the CVPR, IEEE.","DOI":"10.1109\/CVPR52733.2024.00938"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Potamias, R.A., Zhang, J., Deng, J., and Zafeiriou, S. (2025). Wilor: End-to-end 3d hand localization and reconstruction in-the-wild. Proceedings of the IEEE\/CVF Computer Vision and Pattern Recognition Conference (CVPR), IEEE.","DOI":"10.1109\/CVPR52734.2025.01143"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zimmermann, C., Ceylan, D., Yang, J., Russell, B., Argus, M., and Brox, T. (2019). FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape From Single RGB Images. Proceedings of the ICCV, IEEE.","DOI":"10.1109\/ICCV.2019.00090"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Moon, G., Yu, S.I., Wen, H., Shiratori, T., and Lee, K.M. (2020). InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image. Proceedings of the ECCV, IEEE.","DOI":"10.1007\/978-3-030-58565-5_33"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Xiang, D., Joo, H., and Sheikh, Y. (2018). Monocular Total Capture: Posing Face, Body, and Hands in the Wild. CVPR, 10957\u201310966.","DOI":"10.1109\/CVPR.2019.01122"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zimmermann, C., and Brox, T. (2017). Learning to Estimate 3D Hand Pose From Single RGB Images. Proceedings of the ICCV, IEEE.","DOI":"10.1109\/ICCV.2017.525"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Jin, S., Xu, L., Xu, J., Wang, C., Liu, W., Qian, C., Ouyang, W., and Luo, P. (2020). Whole-body human pose estimation in the wild. Proceedings of the ECCV, Springer.","DOI":"10.1007\/978-3-030-58545-7_12"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Fang, H.S., Li, J., Tang, H., Xu, C., Zhu, H., Xiu, Y., Li, Y.L., and Lu, C. (2022). AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time. arXiv.","DOI":"10.1109\/TPAMI.2022.3222784"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Simon, T., Joo, H., Matthews, I., and Sheikh, Y. (2017). Hand keypoint detection in single images using multiview bootstrapping. Proceedings of the CVPR, IEEE.","DOI":"10.1109\/CVPR.2017.494"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lin, K., Wang, L., and Liu, Z. (2020). End-to-End Human Pose and Mesh Reconstruction with Transformers. CVPR, 1954\u20131963.","DOI":"10.1109\/CVPR46437.2021.00199"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Jillani, H.A., Aboukhadra, A.T., Elhayek, A., Malik, J., Robertini, N., and Stricker, D. (2026). Fast-HaMeR: Boosting Hand Mesh Reconstruction using Knowledge Distillation. Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP 2026), Marbella, Spain, 9\u201311 March 2026, University of Strathclyde.","DOI":"10.5220\/0014308900004084"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Lin, K., Wang, L., and Liu, Z. (2021). Mesh Graphormer. Proceedings of the ICCV, IEEE.","DOI":"10.1109\/ICCV48922.2021.01270"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zhao, W., Wang, W., and Tian, Y. (2022). GraFormer: Graph-oriented transformer for 3D pose estimation. Proceedings of the Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.01979"},{"key":"ref_70","unstructured":"Cheng, W., Kim, E.J., and Ko, J.H. (2024). HandDAGT: Towards Effective Graph-based 3D Hand Pose Estimation with Adaptive Graph Transformer. Proceedings of the European Conference on Computer Vision (ECCV), IEEE."},{"key":"ref_71","unstructured":"Saleem, M.U., Pinyoanuntapong, E., and Wang, P. (2025). MaskHand: Learning Generalized 3D Hand Mesh Reconstruction in the Wild via Context-Guided Masked Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Chen, Y., Gao, X., Yan, Y., Wang, Y., and Wang, X. (2025). HORT: Human-Centric 3D Hand-Held Objects Reconstruction via Conditional Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51701.2025.00571"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Hasson, Y., Varol, G., Schmid, C., and Laptev, I. (2021). Towards unconstrained joint hand-object reconstruction from rgb videos. Proceedings of the International Conference on 3D Vision (3DV), IEEE.","DOI":"10.1109\/3DV53792.2021.00075"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Chen, Z., Chen, S., Schmid, C., and Laptev, I. (2023). gsdf: Geometry-driven signed distance functions for 3d hand-object reconstruction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52729.2023.01239"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Fan, Z., Taheri, O., Tzionas, D., Kocabas, M., Kaufmann, M., Black, M.J., and Hilliges, O. (2023). ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52729.2023.01244"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., and Ng, R. (2020). NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. Proceedings of the European Conference on Computer Vision (ECCV), IEEE.","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, B., and Shum, H.Y. (2023). Hand avatar: Free-pose hand animation and rendering from monocular video. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52729.2023.00839"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023). Segment anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_79","unstructured":"Cheng, Y., Li, L., Xu, Y., Li, X., Yang, Z., Wang, W., and Yang, Y. (2023). Segment and Track Anything. arXiv."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., DeTone, D., Malisiewicz, T., and Rabinovich, A. (2020). SuperGlue: Learning Feature Matching with Graph Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., and Frahm, J.M. (2016). Structure-from-Motion Revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., Cadena, C., Siegwart, R., and Dymczyk, M. (2019). From Coarse to Fine: Robust Hierarchical Localization at Large Scale. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.01300"},{"key":"ref_83","first-page":"139:1","article-title":"3D Gaussian Splatting for Real-Time Radiance Field Rendering","volume":"42","author":"Kerbl","year":"2023","journal-title":"ACM Trans. Graph. (TOG) Proc. SIGGRAPH"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2816795.2818013","article-title":"SMPL: A skinned multi-person linear model","volume":"34","author":"Loper","year":"2015","journal-title":"ACM Trans. Graph."},{"key":"ref_85","first-page":"1","article-title":"Learning a model of facial shape and expression from 4D scans","volume":"36","author":"Li","year":"2017","journal-title":"ACM Trans. Graph."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Qian, S., Kirschstein, T., Schoneveld, L., Davoli, D., Giebenhain, S., and Nie\u00dfner, M. (2024). Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.01919"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Pang, H., Zhu, H., Kortylewski, A., Theobalt, C., and Habermann, M. (2024). ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00117"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Shao, Z., Wang, Z., Li, Z., Wang, D., Lin, X., Zhang, Y., Fan, M., and Wang, Z. (2024). Splattingavatar: Realistic real-time human avatars with mesh-embedded gaussian splatting. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00159"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Moreau, A., Song, J., Dhamo, H., Shaw, R., Zhou, Y., and P\u00e9rez-Pellitero, E. (2024). Human gaussian splatting: Real-time rendering of animatable avatars. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00081"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Chang, J.H.R., Gabriel, J., Tuzel, O., and Ranjan, A. (2024). Hugs: Human gaussian splats. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00055"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Pokhariya, C., Shah, I.N., Xing, A., Li, Z., Chen, K., Sharma, A., and Sridhar, S. (2024). MANUS: Markerless Grasp Capture using Articulated 3D Gaussians. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00214"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.52202\/079017-0069","article-title":"Hamba: Single-view 3d hand reconstruction with graph-guided bi-scanning mamba","volume":"37","author":"Dong","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Tang, D., Jin Chang, H., Tejani, A., and Kim, T.K. (2014). Latent regression forest: Structured estimation of 3d articulated hand posture. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2014.490"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1109\/TPAMI.2017.2782743","article-title":"Panoptic Studio: A Massively Multiview System for Social Interaction Capture","volume":"41","author":"Joo","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Li, Y., Wu, M., Zhang, Y., Xu, L., and Yu, J. (2021). PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, IEEE.","DOI":"10.24963\/ijcai.2021\/113"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, L., Qiu, Z., Jiang, Y., Li, N., Ma, Y., Zhang, Y., Xu, L., and Yu, J. (2022). NIMBLE: A Non-rigid Hand Model with Bones and Muscles. arXiv.","DOI":"10.1145\/3528223.3530079"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A.A.A., Tzionas, D., and Black, M.J. (2019). Expressive Body Capture: 3D Hands, Face, and Body from a Single Image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.01123"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Feng, Y., Lin, J., Dwivedi, S.K., Sun, Y., Patel, P., and Black, M.J. (2024). ChatPose: Chatting about 3D Human Pose. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00204"},{"key":"ref_99","unstructured":"Lin, J., Feng, Y., Liu, W., and Black, M.J. (2024). ChatHuman: Language-driven 3D Human Understanding with Retrieval-Augmented Tool Reasoning. arXiv."},{"key":"ref_100","unstructured":"Wang, W., Fang, H.S., Liu, Z., and Wang, C. (2024). HandsOnVLM: Vision-Language Models for Hand-Object Interaction Prediction. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Neural Information Processing Systems Foundation, Inc."},{"key":"ref_101","unstructured":"Zhang, Y., Chen, L., Li, H., and Liu, M. (2023). CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting. arXiv."},{"key":"ref_102","unstructured":"Li, X., Zhao, R., Huang, S., and Chen, X. (2025). Affordance-Guided Hand Pose Estimation with Vision-Language Models. arXiv."},{"key":"ref_103","first-page":"446","article-title":"Hands in the Wild Benchmark","volume":"Volume 15065","author":"Leonardis","year":"2024","journal-title":"Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Chen, X., Liu, Y., Dong, Y., Zhang, X., Ma, C., and Xiong, Y. (2022). MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.01989"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Kephart, J.O., and Ji, Q. (2024). Weakly-Supervised 3D Hand Reconstruction with Structured Priors and Uncertainty Modeling. IEEE Trans. Pattern Anal. Mach. Intell., 106\u2013125.","DOI":"10.1007\/978-3-031-73229-4_7"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/425\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:37:38Z","timestamp":1788950258000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":105,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jimaging12090425"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12090425","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]}}}