{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T06:18:54Z","timestamp":1761718734213,"version":"3.40.3"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030012335"},{"type":"electronic","value":"9783030012342"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01234-2_9","type":"book-chapter","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T16:13:11Z","timestamp":1538755991000},"page":"142-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7983-1461","authenticated-orcid":false,"given":"Junwu","family":"Weng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4036-9315","authenticated-orcid":false,"given":"Mengyuan","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9104-2315","authenticated-orcid":false,"given":"Xudong","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7324-7034","authenticated-orcid":false,"given":"Junsong","family":"Yuan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,10,6]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. In: CVPR, pp. 1297\u20131304. IEEE (2011)","key":"9_CR1","DOI":"10.1109\/CVPR.2011.5995316"},{"doi-asserted-by":"crossref","unstructured":"Ge, L., Cai, Y., Weng, J., Yuan, J.: Hand PointNet: 3D hand pose estimation using point sets. In: CVPR, vol. 1, p. 5 (2018)","key":"9_CR2","DOI":"10.1109\/CVPR.2018.00878"},{"key":"9_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1007\/978-3-030-01261-8_29","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L Ge","year":"2018","unstructured":"Ge, L., Ren, Z., Yuan, J.: Point-to-point regression PointNet for 3D hand pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part XIII. LNCS, vol. 11217, pp. 489\u2013505. Springer, Cham (2018)"},{"doi-asserted-by":"crossref","unstructured":"Song, S., Lan, C., Xing, J., Zeng, W., Liu, J.: An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In: AAAI, vol. 1, p. 7 (2017)","key":"9_CR4","DOI":"10.1609\/aaai.v31i1.11212"},{"doi-asserted-by":"crossref","unstructured":"Weng, J., Weng, C., Yuan, J.: Spatio-temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for skeleton-based action recognition. In: CVPR, pp. 4171\u20134180 (2017)","key":"9_CR5","DOI":"10.1109\/CVPR.2017.55"},{"doi-asserted-by":"crossref","unstructured":"Liu, J., Wang, G., Hu, P., Duan, L.Y., Kot, A.C.: Global context-aware attention LSTM networks for 3D action recognition. In: CVPR, July 2017","key":"9_CR6","DOI":"10.1109\/CVPR.2017.391"},{"key":"9_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1007\/978-3-319-16814-2_4","volume-title":"Computer Vision \u2013 ACCV 2014","author":"G Yu","year":"2015","unstructured":"Yu, G., Liu, Z., Yuan, J.: Discriminative orderlet mining for real-time recognition of human-object interaction. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9007, pp. 50\u201365. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16814-2_4"},{"doi-asserted-by":"crossref","unstructured":"Veeriah, V., Zhuang, N., Qi, G.J.: Differential recurrent neural networks for action recognition. In: ICCV, pp. 4041\u20134049. IEEE (2015)","key":"9_CR8","DOI":"10.1109\/ICCV.2015.460"},{"doi-asserted-by":"crossref","unstructured":"Zhu, W., et al.: Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: AAAI, vol. 2, p. 8 (2016)","key":"9_CR9","DOI":"10.1609\/aaai.v30i1.10451"},{"doi-asserted-by":"crossref","unstructured":"Li, W., Wen, L., Chang, M.C., Nam Lim, S., Lyu, S.: Adaptive RNN tree for large-scale human action recognition. In: ICCV, October 2017","key":"9_CR10","DOI":"10.1109\/ICCV.2017.161"},{"doi-asserted-by":"crossref","unstructured":"Lee, I., Kim, D., Kang, S., Lee, S.: Ensemble deep learning for skeleton-based action recognition using temporal sliding LSTM networks. In: ICCV, October 2017","key":"9_CR11","DOI":"10.1109\/ICCV.2017.115"},{"unstructured":"Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: CVPR, pp. 1110\u20131118 (2015)","key":"9_CR12"},{"doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: NTU RGB+D: a large scale dataset for 3D human activity analysis. In: CVPR, June 2016","key":"9_CR13","DOI":"10.1109\/CVPR.2016.115"},{"key":"9_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1007\/978-3-319-46487-9_50","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Liu","year":"2016","unstructured":"Liu, J., Shahroudy, A., Xu, D., Wang, G.: Spatio-temporal LSTM with trust gates for 3D human action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 816\u2013833. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_50"},{"doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, L.: Modeling temporal dynamics and spatial configurations of actions using two-stream recurrent neural networks. In: CVPR, July 2017","key":"9_CR15","DOI":"10.1109\/CVPR.2017.387"},{"doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: ICCV, October 2017","key":"9_CR16","DOI":"10.1109\/ICCV.2017.89"},{"unstructured":"Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: NIPS, pp. 802\u2013810 (2015)","key":"9_CR17"},{"issue":"8","key":"9_CR18","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"doi-asserted-by":"crossref","unstructured":"Ren, Z., Yuan, J., Zhang, Z.: Robust hand gesture recognition based on finger-earth mover\u2019s distance with a commodity depth camera. In: ACM MM, pp. 1093\u20131096 (2011)","key":"9_CR19","DOI":"10.1145\/2072298.2071946"},{"unstructured":"Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: CVPR, pp. 1290\u20131297. IEEE (2012)","key":"9_CR20"},{"issue":"5","key":"9_CR21","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1109\/TPAMI.2013.198","volume":"36","author":"J Wang","year":"2014","unstructured":"Wang, J., Liu, Z., Wu, Y., Yuan, J.: Learning actionlet ensemble for 3D human action recognition. T-PAMI 36(5), 914\u2013927 (2014)","journal-title":"T-PAMI"},{"doi-asserted-by":"crossref","unstructured":"Liang, H., Yuan, J., Thalmann, D., Thalmann, N.M.: AR in hand: egocentric palm pose tracking and gesture recognition for augmented reality applications. In: ACM MM, pp. 743\u2013744. ACM (2015)","key":"9_CR22","DOI":"10.1145\/2733373.2807972"},{"issue":"5","key":"9_CR23","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1109\/TMM.2013.2246148","volume":"15","author":"Zhou Ren","year":"2013","unstructured":"Ren, Z., Yuan, J., Meng, J., Zhang, Z.: Robust part-based hand gesture recognition using Kinect sensor. T-MM 15(5), 1110\u20131120 (2016)","journal-title":"IEEE Transactions on Multimedia"},{"doi-asserted-by":"crossref","unstructured":"Weng, J., Weng, C., Yuan, J., Liu, Z.: Discriminative spatio-tempoal pattern discovery for 3D action recognition. T-CSVT, PP, 1 (2018)","key":"9_CR24","DOI":"10.1109\/TCSVT.2018.2818151"},{"issue":"1","key":"9_CR25","first-page":"24","volume":"25","author":"F Ofli","year":"2014","unstructured":"Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Sequence of the most informative joints (SMIJ): a new representation for human skeletal action recognition. JVCI 25(1), 24\u201338 (2014)","journal-title":"JVCI"},{"doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., Chellapa, R.: Rolling rotations for recognizing human actions from 3D skeletal data. In: CVPR, pp. 4471\u20134479 (2016)","key":"9_CR26","DOI":"10.1109\/CVPR.2016.484"},{"doi-asserted-by":"crossref","unstructured":"Garcia-Hernando, G., Kim, T.K.: Transition forests: learning discriminative temporal transitions for action recognition and detection. In: CVPR, pp. 432\u2013440 (2017)","key":"9_CR27","DOI":"10.1109\/CVPR.2017.51"},{"key":"9_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1007\/978-3-319-46478-7_23","volume-title":"Computer Vision \u2013 ECCV 2016","author":"P Wang","year":"2016","unstructured":"Wang, P., Yuan, C., Hu, W., Li, B., Zhang, Y.: Graph based skeleton motion representation and similarity measurement for action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 370\u2013385. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_23"},{"doi-asserted-by":"crossref","unstructured":"De Smedt, Q., Wannous, H., Vandeborre, J.P.: Skeleton-based dynamic hand gesture recognition. In: CVPRW, pp. 1\u20139 (2016)","key":"9_CR29","DOI":"10.1109\/CVPRW.2016.153"},{"doi-asserted-by":"crossref","unstructured":"Liu, M., Yuan, J.: Recognizing human actions as the evolution of pose estimation maps. In: CVPR, June 2018","key":"9_CR30","DOI":"10.1109\/CVPR.2018.00127"},{"key":"9_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-319-46478-7_13","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Li","year":"2016","unstructured":"Li, Y., Lan, C., Xing, J., Zeng, W., Yuan, C., Liu, J.: Online human action detection using joint classification-regression recurrent neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 203\u2013220. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_13"},{"doi-asserted-by":"crossref","unstructured":"Ke, Q., Bennamoun, M., An, S., Sohel, F., Boussaid, F.: A new representation of skeleton sequences for 3D action recognition. In: CVPR, July 2017","key":"9_CR32","DOI":"10.1109\/CVPR.2017.486"},{"doi-asserted-by":"crossref","unstructured":"Wang, P., Li, Z., Hou, Y., Li, W.: Action recognition based on joint trajectory maps using convolutional neural networks. In: ACM MM, pp. 102\u2013106. ACM (2016)","key":"9_CR33","DOI":"10.1145\/2964284.2967191"},{"key":"9_CR34","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.patcog.2017.02.030","volume":"68","author":"M Liu","year":"2017","unstructured":"Liu, M., Liu, H., Chen, C.: Enhanced skeleton visualization for view invariant human action recognition. Pattern Recogn. 68, 346\u2013362 (2017)","journal-title":"Pattern Recogn."},{"doi-asserted-by":"crossref","unstructured":"Lea, C., Flynn, M.D., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks for action segmentation and detection. In: CVPR, July 2017","key":"9_CR35","DOI":"10.1109\/CVPR.2017.113"},{"key":"9_CR36","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-642-24797-2_2","volume-title":"Studies in Computational Intelligence","author":"Alex Graves","year":"2012","unstructured":"Graves, A.: Supervised sequence labelling. In: Graves, A. (eds.) Supervised Sequence Labelling with Recurrent Neural Networks, pp. 5\u201313. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-24797-2_2"},{"doi-asserted-by":"crossref","unstructured":"Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Berkeley MHAD: a comprehensive multimodal human action database. In: WACV, pp. 53\u201360. IEEE (2013)","key":"9_CR37","DOI":"10.1109\/WACV.2013.6474999"},{"doi-asserted-by":"crossref","unstructured":"Evangelidis, G., Singh, G., Horaud, R.: Skeletal quads: human action recognition using joint quadruples. In: ICPR, pp. 4513\u20134518. IEEE (2014)","key":"9_CR38","DOI":"10.1109\/ICPR.2014.772"},{"unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization (2015)","key":"9_CR39"},{"unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS, pp. 3104\u20133112 (2014)","key":"9_CR40"},{"unstructured":"Li, C., Zhong, Q., Xie, D., Pu, S.: Skeleton-based action recognition with convolutional neural networks. In: ICMEW, pp. 597\u2013600. IEEE (1997)","key":"9_CR41"},{"doi-asserted-by":"crossref","unstructured":"Vantigodi, S., Radhakrishnan, V.B.: Action recognition from motion capture data using meta-cognitive RBF network classifier. In: ISSNIP, pp. 1\u20136. IEEE (2014)","key":"9_CR42","DOI":"10.1109\/ISSNIP.2014.6827664"},{"issue":"6","key":"9_CR43","first-page":"1432","volume":"25","author":"I Kapsouras","year":"2014","unstructured":"Kapsouras, I., Nikolaidis, N.: Action recognition on motion capture data using a dynemes and forward differences representation. JVCI 25(6), 1432\u20131445 (2014)","journal-title":"JVCI"},{"doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR, vol. 1, p. 7 (2017)","key":"9_CR44","DOI":"10.1109\/CVPR.2017.143"},{"key":"9_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1007\/978-3-030-01231-1_41","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Cai","year":"2018","unstructured":"Cai, Y., Ge, L., Cai, J., Yuan, J.: Weakly-supervised 3D hand pose estimation from monocular RGB images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, Part VI. LNCS, vol. 11210, pp. 678\u2013694. Springer, Cham (2018)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01234-2_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:18:33Z","timestamp":1664929113000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01234-2_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030012335","9783030012342"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01234-2_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"6 October 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}