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A data-driven approach is used to learn human joint limits from 3D motion capture datasets. We represent joint constraints with a new formulation (<jats:italic>s<\/jats:italic>\n                  <jats:sub>1<\/jats:sub>,<jats:italic>s<\/jats:italic>\n                  <jats:sub>2<\/jats:sub>,<jats:italic>\u03c4<\/jats:italic>) using swing-twist representation in exponential maps form. Our parameterization is applied on Human3.6M dataset to create the lookup-map for each joint. These maps enable us to generate \u2018synthetic\u2019 datasets in entire joint rotation space of a given joint. A set of neural network discriminators is then trained with synthetic datasets to learn valid\/invalid joint rotations. The discriminators achieve accuracy of [94.4\u221299.4<jats:italic>%<\/jats:italic>] for different joints. We validate precision-accuracy trade-off of discriminators and qualitatively evaluate classified poses with an interactive tool. The learned discriminators can be used as \u2018priors\u2019 for human pose estimation and motion synthesis.<\/jats:p>","DOI":"10.1186\/s41074-019-0057-z","type":"journal-article","created":{"date-parts":[[2019,6,25]],"date-time":"2019-06-25T15:02:46Z","timestamp":1561474966000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning 3D joint constraints from vision-based motion capture datasets"],"prefix":"10.1186","volume":"11","author":[{"given":"Pramod","family":"Murthy","sequence":"first","affiliation":[]},{"given":"Hammad T.","family":"Butt","sequence":"additional","affiliation":[]},{"given":"Sandesh","family":"Hiremath","sequence":"additional","affiliation":[]},{"given":"Alireza","family":"Khoshhal","sequence":"additional","affiliation":[]},{"given":"Didier","family":"Stricker","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,25]]},"reference":[{"key":"57_CR1","doi-asserted-by":"crossref","unstructured":"Kanazawa A, Black MJ, Jacobs DW, Malik J (2018) End-to-end recovery of human shape and pose In: Proceedings of the IEEE Conference on Computer Vision and Pattern Regognition (CVPR): 19-21 June 2018; Salt Lake City Utah, USA, 7122\u20137131.. IEEE.","DOI":"10.1109\/CVPR.2018.00744"},{"issue":"4","key":"57_CR2","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1145\/3072959.3073596","volume":"36","author":"D Mehta","year":"2017","unstructured":"Mehta D, Sridhar S, Sotnychenko O, Rhodin H, Shafiei M, Seidel H-P, Xu W, Casas D, Theobalt C (2017) Vnect: Real-time 3d human pose estimation with a single RGB camera. ACM Trans Graph (TOG) 36(4):44.","journal-title":"ACM Trans Graph (TOG)"},{"key":"57_CR3","first-page":"186","volume-title":"European Conference on Computer Vision (ECCV): 8-16 October 2016; Amsterdam, Netherlands","author":"X Zhou","year":"2016","unstructured":"Zhou X, Sun X, Zhang W, Liang S, Wei Y (2016) Deep kinematic pose regression In: European Conference on Computer Vision (ECCV): 8-16 October 2016; Amsterdam, Netherlands, 186\u2013201.. 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