{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T13:17:05Z","timestamp":1772111825536,"version":"3.50.1"},"reference-count":45,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T00:00:00Z","timestamp":1628380800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T00:00:00Z","timestamp":1628380800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,8]]},"DOI":"10.1109\/ro-man50785.2021.9515316","type":"proceedings-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T18:07:53Z","timestamp":1629742073000},"page":"641-648","source":"Crossref","is-referenced-by-count":14,"title":["Graph-based Normalizing Flow for Human Motion Generation and Reconstruction"],"prefix":"10.1109","author":[{"given":"Wenjie","family":"Yin","sequence":"first","affiliation":[{"name":"KTH Royal Institute of Technology,Robotics, Perception and Learning lab,Sweden"}]},{"given":"Hang","family":"Yin","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology,Robotics, Perception and Learning lab,Sweden"}]},{"given":"Danica","family":"Kragic","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology,Robotics, Perception and Learning lab,Sweden"}]},{"given":"Marten","family":"Bjorkman","sequence":"additional","affiliation":[{"name":"KTH Royal Institute of Technology,Robotics, Perception and Learning lab,Sweden"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Graph normalizing flows","author":"liu","year":"2019"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2995383"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018989"},{"key":"ref32","article-title":"Part-based graph convolutional net-work for action recognition","author":"thakkar","year":"2018"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00132"},{"key":"ref30","article-title":"Group behavior recognition using attention-and graph-based neural networks","author":"yang","year":"2020","journal-title":"Proceedings of the 24th European Conference on Artificial Intelligence"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/RO-MAN47096.2020.9223584"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00354"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref34","first-page":"214","article-title":"Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction","author":"li","year":"2020","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"ref10","article-title":"Auto-conditioned recurrent networks for extended complex human motion synthesis","author":"li","year":"2017"},{"key":"ref40","article-title":"Nice: Non-linear independent components estimation","author":"dinh","year":"2014"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00449"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461967"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3382507.3418815"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1111\/cgf.14116","article-title":"Probabilistic character motion synthesis using a hierarchical deep latent variable model","volume":"39","author":"ghorbani","year":"2020","journal-title":"Computer Graphics Forum"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2017-894"},{"key":"ref16","first-page":"10 215","article-title":"Glow: Generative flow with invertible 1x1 convolutions","author":"kingma","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13946"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2015.04.004"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICSP.2016.7877975"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00371"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.173"},{"key":"ref27","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.12328","article-title":"Spatial temporal graph convolutional networks for skeleton-based action recognition","volume":"32","author":"yan","year":"2018","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.494"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2019.2938520"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00026"},{"key":"ref5","article-title":"On human motion pre-diction using recurrent neural networks","author":"martinez","year":"2017","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref8","article-title":"A recurrent variational autoencoder for human motion synthesis","author":"habibie","year":"2017","journal-title":"28th British Machine Vision Conference"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2977333"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2559636.2559665"},{"key":"ref9","article-title":"A neural network approach to missing marker reconstruction in human motion capture","author":"kucherenko","year":"2018"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417836"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0152616"},{"key":"ref45","article-title":"Efficient and robust anno-tation of motion capture data","author":"m\u00fcller","year":"0","journal-title":"Proceedings of the ACM SIG-GRAPH\/Eurographics Symposium on Computer Animation (SCA)"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2687044"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1049\/el.2013.0442"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.5244\/C.31.119"},{"key":"ref24","article-title":"A deep recurrent framework for cleaning motion capture data","author":"mall","year":"2017"},{"key":"ref41","article-title":"Density estimation using real nvp","author":"dinh","year":"2016"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.04.031"},{"key":"ref44","year":"0"},{"key":"ref26","first-page":"2342","article-title":"Recovering trajectories of unmarked joints in 3d human actions using latent space optimization","author":"lohit","year":"2021","journal-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/99"}],"event":{"name":"2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)","location":"Vancouver, BC, Canada","start":{"date-parts":[[2021,8,8]]},"end":{"date-parts":[[2021,8,12]]}},"container-title":["2021 30th IEEE International Conference on Robot &amp; Human Interactive Communication (RO-MAN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9515344\/9515312\/09515316.pdf?arnumber=9515316","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T17:35:34Z","timestamp":1673112934000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9515316\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,8]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/ro-man50785.2021.9515316","relation":{},"subject":[],"published":{"date-parts":[[2021,8,8]]}}}