This project is part of the multi-sense efforts within the people centric strategy of Microsoft. It addresses a number of vertical domains for AI by developing effective human-centric spatial understanding technologies to extract insights from the video data that aim to help us better understand the physical space around us. The core technologies include human detection\/tracking, multi-view 3D human pose estimation, person re-identification across multiple cameras, action recognition, unsupervised video representation learning, as well as domain generalization and adaptation technologies that help scale the solutions to different application settings. Although these technologies were largely developed as part of our ongoing collaborations with the Dynamics\/Connected Store team, the Office Media Group, and the AI\/Cognition team, they are generic and are applicable to many application scenarios.<\/p>\n
Related Links:<\/p>\n
This project is part of the multi-sense efforts within the people centric strategy of Microsoft. It addresses a number of vertical domains for AI by developing effective human-centric spatial understanding technologies to extract insights from the video data that aim to help us better understand the physical space around us. The core technologies include human […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13551],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-733489","msr-project","type-msr-project","status-publish","hentry","msr-research-area-graphics-and-multimedia","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"2019-12-01","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Cuiling Lan","user_id":31487,"people_section":"Section name 0","alias":"culan"},{"type":"user_nicename","display_name":"Chong Luo","user_id":31450,"people_section":"Section name 0","alias":"cluo"},{"type":"user_nicename","display_name":"Wenxuan Xie","user_id":34826,"people_section":"Section name 0","alias":"wenxie"}],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/733489"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/733489\/revisions"}],"predecessor-version":[{"id":733498,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/733489\/revisions\/733498"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=733489"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=733489"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=733489"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=733489"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=733489"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}