{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T03:15:45Z","timestamp":1770693345138,"version":"3.49.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Liaoning Provincial Science Initiative","award":["2021JH1\/1040011"],"award-info":[{"award-number":["2021JH1\/1040011"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s13042-025-02826-3","type":"journal-article","created":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T07:38:20Z","timestamp":1768635500000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["3D hand pose estimation based on relational graph attention mechanism"],"prefix":"10.1007","volume":"17","author":[{"given":"Lihuang","family":"She","sequence":"first","affiliation":[]},{"given":"Haonan","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Xiangli","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yehan","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,17]]},"reference":[{"key":"2826_CR1","doi-asserted-by":"crossref","unstructured":"Wan C, Probst T, Van Gool L, Yao A (2018) Dense 3d regression for hand pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5147\u20135156","DOI":"10.1109\/CVPR.2018.00540"},{"key":"2826_CR2","doi-asserted-by":"crossref","unstructured":"Graham B, Engelcke M, Van Der Maaten L (2018) 3d semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 9224\u20139232","DOI":"10.1109\/CVPR.2018.00961"},{"key":"2826_CR3","doi-asserted-by":"crossref","unstructured":"Klokov R, Lempitsky V (2017) Escape from cells: Deep kd-networks for the recognition of 3d point cloud models. In: Proceedings of the IEEE International Conference on Computer Vision, pp 863\u2013872","DOI":"10.1109\/ICCV.2017.99"},{"issue":"12","key":"2826_CR4","doi-asserted-by":"publisher","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","volume":"43","author":"Y Guo","year":"2020","unstructured":"Guo Y, Wang H, Hu Q, Liu H, Liu L, Bennamoun M (2020) Deep learning for 3d point clouds: a survey. IEEE Trans Pattern Anal Mach Intell 43(12):4338\u20134364","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2826_CR5","doi-asserted-by":"crossref","unstructured":"Cheng W, Tang H, Van Gool L, Ko JH (2024) Handdiff: 3d hand pose estimation with diffusion on image-point cloud. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2274\u20132284","DOI":"10.1109\/CVPR52733.2024.00221"},{"key":"2826_CR6","unstructured":"Busbridge D, Sherburn D, Cavallo P, Hammerla NY (2019) Relational graph attention networks. arXiv preprint arXiv:1904.05811"},{"key":"2826_CR7","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"issue":"5","key":"2826_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2629500","volume":"33","author":"J Tompson","year":"2014","unstructured":"Tompson J, Stein M, Lecun Y, Perlin K (2014) Real-time continuous pose recovery of human hands using convolutional networks. ACM Trans Graphics (ToG) 33(5):1\u201310","journal-title":"ACM Trans Graphics (ToG)"},{"key":"2826_CR9","unstructured":"Oberweger M, Wohlhart P, Lepetit V (2015) Hands deep in deep learning for hand pose estimation arXiv preprint arXiv:1502.06807"},{"key":"2826_CR10","unstructured":"Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 652\u2013660"},{"key":"2826_CR11","doi-asserted-by":"crossref","unstructured":"Ge L, Liang H, Yuan J, Thalmann D (2016) Robust 3d hand pose estimation in single depth images: from single-view cnn to multi-view cnns. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3593\u20133601","DOI":"10.1109\/CVPR.2016.391"},{"key":"2826_CR12","doi-asserted-by":"crossref","unstructured":"Ge L, Cai Y, Weng J, Yuan J (2018) Hand pointnet: 3d hand pose estimation using point sets. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8417\u20138426","DOI":"10.1109\/CVPR.2018.00878"},{"key":"2826_CR13","doi-asserted-by":"publisher","first-page":"43425","DOI":"10.1109\/ACCESS.2018.2863540","volume":"6","author":"X Chen","year":"2018","unstructured":"Chen X, Wang G, Zhang C, Kim T-K, Ji X (2018) Shpr-net: deep semantic hand pose regression from point clouds. IEEE Access 6:43425\u201343439","journal-title":"IEEE Access"},{"key":"2826_CR14","doi-asserted-by":"crossref","unstructured":"Cheng W, Park JH, Ko JH (2021) Handfoldingnet: a 3d hand pose estimation network using multiscale-feature guided folding of a 2d hand skeleton. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 11260\u201311269","DOI":"10.1109\/ICCV48922.2021.01107"},{"key":"2826_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119922","volume":"223","author":"M Rezaei","year":"2023","unstructured":"Rezaei M, Rastgoo R, Athitsos V (2023) Trihorn-net: a model for accurate depth-based 3d hand pose estimation. Expert Syst Appl 223:119922","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2826_CR16","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Trans Neural Networks 20(1):61\u201380","journal-title":"IEEE Trans Neural Networks"},{"issue":"6","key":"2826_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3363574","volume":"13","author":"JB Lee","year":"2019","unstructured":"Lee JB, Rossi RA, Kim S, Ahmed NK, Koh E (2019) Attention models in graphs: a survey. ACM Trans Knowl Disc Data (TKDD) 13(6):1\u201325","journal-title":"ACM Trans Knowl Disc Data (TKDD)"},{"key":"2826_CR18","unstructured":"Zhang J, Shi X, Xie J, Ma H, King I, Yeung D-Y (2018) Gaan: gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294"},{"issue":"11","key":"2826_CR19","doi-asserted-by":"publisher","first-page":"7583","DOI":"10.1007\/s11760-024-03412-w","volume":"18","author":"I Bulugu","year":"2024","unstructured":"Bulugu I (2024) Adaptive shift graph convolutional neural network for hand gesture recognition based on 3d skeletal similarity. SIViP 18(11):7583\u20137595","journal-title":"SIViP"},{"key":"2826_CR20","doi-asserted-by":"crossref","unstructured":"Ikne O, Allaert B, Wannous H (2024). Skeleton-based self-supervised feature extraction for improved dynamic hand gesture recognition. In: 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pp 1\u201310 IEEE","DOI":"10.1109\/FG59268.2024.10581975"},{"key":"2826_CR21","doi-asserted-by":"crossref","unstructured":"Ikne O, Slama R, Saoudi H, Wannous H (2024). Spatio-temporal sparse graph convolution network for hand gesture recognition. In: 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pp 1\u20135 IEEE","DOI":"10.1109\/FG59268.2024.10581903"},{"key":"2826_CR22","doi-asserted-by":"crossref","unstructured":"Feghoul K, Maia DS, El Amrani M, Daoudi M, Amad A (2024) Mgrformer: a multimodal transformer approach for surgical gesture recognition. In: 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pp 1\u201310 IEEE","DOI":"10.1109\/FG59268.2024.10581962"},{"key":"2826_CR23","doi-asserted-by":"crossref","unstructured":"\u00d6zdemir O, Bayta\u015f \u0130M, Akarun L (2024) Hand graph topology selection for skeleton-based sign language recognition. In: 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pp 1\u20135 IEEE","DOI":"10.1109\/FG59268.2024.10581950"},{"issue":"4","key":"2826_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3490181","volume":"40","author":"H Peng","year":"2021","unstructured":"Peng H, Zhang R, Dou Y, Yang R, Zhang J, Yu PS (2021) Reinforced neighborhood selection guided multi-relational graph neural networks. ACM Trans Inf Syst (TOIS) 40(4):1\u201346","journal-title":"ACM Trans Inf Syst (TOIS)"},{"issue":"6","key":"2826_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3660522","volume":"42","author":"H Peng","year":"2024","unstructured":"Peng H, Zhang J, Huang X, Hao Z, Li A, Yu Z, Yu PS (2024) Unsupervised social bot detection via structural information theory. ACM Trans Inform Syst 42(6):1\u201342","journal-title":"ACM Trans Inform Syst"},{"issue":"1","key":"2826_CR26","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1109\/TPAMI.2022.3144993","volume":"45","author":"H Peng","year":"2022","unstructured":"Peng H, Zhang R, Li S, Cao Y, Pan S, Yu PS (2022) Reinforced, incremental and cross-lingual event detection from social messages. IEEE Trans Pattern Anal Mach Intell 45(1):980\u2013998","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2826_CR27","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272 . PMLR"},{"key":"2826_CR28","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems 29"},{"key":"2826_CR29","doi-asserted-by":"crossref","unstructured":"Wang L, Chen X, Xiong W, Li Z (2022). Research on gesture recognition and classification based on attention mechanism, In: 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol 5. pp 1617\u20131621 IEEE","DOI":"10.1109\/IMCEC55388.2022.10019998"},{"key":"2826_CR30","doi-asserted-by":"crossref","unstructured":"Kumar AJR, Bhanu B (2023) Relational edge-node graph attention network for classification of micro-expressions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5819\u20135828","DOI":"10.1109\/CVPRW59228.2023.00618"},{"key":"2826_CR31","doi-asserted-by":"crossref","unstructured":"Rehman MU, Ilyas T, Seneviratne L, Hussain I (2024). Enhanced gesture recognition through graph-based multimodal fusion. In: 2024 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp 1\u20135 IEEE","DOI":"10.1109\/ICSPCC62635.2024.10770517"},{"key":"2826_CR32","unstructured":"Nakamura I (2024) Multi-scale spatial-temporal self-attention graph convolutional networks for skeleton-based action recognition. arXiv preprint arXiv:2404.02624"},{"key":"2826_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102789","volume":"115","author":"Z Li","year":"2025","unstructured":"Li Z, Shou D (2025) Gesture recognition with adaptive-weight-based residual multiheadcrossattention fusion based on multi-level feature information. Inform Fusion 115:102789","journal-title":"Inform Fusion"},{"key":"2826_CR34","doi-asserted-by":"crossref","unstructured":"Monti F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein MM (2017) Geometric deep learning on graphs and manifolds using mixture model cnns. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5115\u20135124","DOI":"10.1109\/CVPR.2017.576"},{"issue":"1","key":"2826_CR35","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2826_CR36","unstructured":"Vaswani A (2017) Attention is all you need. Advances in Neural Information Processing Systems"},{"key":"2826_CR37","unstructured":"Jang E, Gu S, Poole B (2016) Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144"},{"key":"2826_CR38","doi-asserted-by":"crossref","unstructured":"Sun X, Wei Y, Liang S, Tang X, Sun J (2015) Cascaded hand pose regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 824\u2013832","DOI":"10.1109\/CVPR.2015.7298683"},{"key":"2826_CR39","doi-asserted-by":"crossref","unstructured":"Tang D, Jin Chang H, Tejani A, Kim T-K (2014) Latent regression forest: structured estimation of 3d articulated hand posture. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3786\u20133793","DOI":"10.1109\/CVPR.2014.490"},{"key":"2826_CR40","doi-asserted-by":"crossref","unstructured":"Oberweger M, Lepetit V (2017) Deepprior++: Improving fast and accurate 3d hand pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp 585\u2013594","DOI":"10.1109\/ICCVW.2017.75"},{"key":"2826_CR41","doi-asserted-by":"crossref","unstructured":"Moon G, Chang JY, Lee KM (2018) V2v-posenet: Voxel-to-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5079\u20135088","DOI":"10.1109\/CVPR.2018.00533"},{"key":"2826_CR42","doi-asserted-by":"crossref","unstructured":"Du K, Lin X, Sun Y, Ma X (2019) Crossinfonet: Multi-task information sharing based hand pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9896\u20139905","DOI":"10.1109\/CVPR.2019.01013"},{"key":"2826_CR43","doi-asserted-by":"crossref","unstructured":"Cheng J, Wan Y, Zuo D, Ma C, Gu J, Tan P, Wang H, Deng X, Zhang Y (2022) Efficient virtual view selection for 3d hand pose estimation. In: Proceedings of the AAAI Conference on Artificial Intelligence 36:419\u2013426","DOI":"10.1609\/aaai.v36i1.19919"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02826-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02826-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02826-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T09:39:10Z","timestamp":1770629950000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02826-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2826"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02826-3","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"17 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2026","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 do not have any relevant financial or nonfinancial interests to report. The authors have no Conflict of interest to declare that would be relevant to the content of this article. All authors certify that they have no affiliation or involvement with any organisation or entity that has any financial or non-financial interest in the subject matter or materials discussed in this manuscript. No material discussed in this article has any financial or proprietary interest for the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"18"}}