{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatio-temporal-graph-transformer-networks","title":"Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction","arxiv_id":"2005.08514","date":"2020-05-18","proceeding":"ECCV 2020 8","authors":["Cunjun Yu","Xiao Ma","Jiawei Ren","Haiyu Zhao","Shuai Yi"],"abstract":"Understanding crowd motion dynamics is critical to real-world applications, e.g., surveillance systems and autonomous driving. 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