{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:42:12Z","timestamp":1765438932069,"version":"3.41.0"},"reference-count":0,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["AI Matters"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:p>\n            Estimating 3D human motion from an ego-centric video, which records the environment viewed from the first-person perspective with a front-facing monocular camera, is critical to applications in VR\/AR. However, naively learning a mapping between egocentric videos and full-body human motions is challenging for two reasons. First, modeling this complex relationship is difficult; unlike reconstruction motion from third-person videos, the human body is often out of view of an egocentric video. Second, learning this mapping requires a large-scale, diverse dataset containing paired egocentric videos and the corresponding 3D human poses. Creating such a dataset requires meticulous instrumentation for data acquisition, and unfortunately, such a dataset does not currently exist. As such, existing works have only worked on small-scale datasets with limited motion and scene diversity\n            <jats:bold>(yuan20183d; yuan2019ego; luo2021dynamics).<\/jats:bold>\n          <\/jats:p>","DOI":"10.1145\/3609468.3609473","type":"journal-article","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T16:44:07Z","timestamp":1696956247000},"page":"20-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Ego-Body Pose Estimation via Ego-Head Pose Estimation"],"prefix":"10.1145","volume":"9","author":[{"given":"Jiaman","family":"Li","sequence":"first","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. Karen","family":"Liu","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajun","family":"Wu","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,10]]},"container-title":["AI Matters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3609468.3609473","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3609468.3609473","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:24Z","timestamp":1750178784000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3609468.3609473"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["10.1145\/3609468.3609473"],"URL":"https:\/\/doi.org\/10.1145\/3609468.3609473","relation":{},"ISSN":["2372-3483"],"issn-type":[{"type":"electronic","value":"2372-3483"}],"subject":[],"published":{"date-parts":[[2023,6]]},"assertion":[{"value":"2023-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}