Real-time understanding of dynamic human presence is crucial for immersive Augmented Reality ({AR}), yet challenging on resource-constrained Head-Mounted Displays ({HMDs}). This paper introduces Rhino-{AR}, a pipeline for ondevice 3D human pose estimation and dynamic scene integration for commercial {AR} headsets like the Magic Leap 2. Our system processes {RGB} and sparse depth data, first detecting 2D keypoints, then robustly lifting them to 3D. Beyond pose estimation, we reconstruct a coarse anatomical model of the human body, tightly coupled with the estimated skeleton. This volumetric proxy for dynamic human geometry is then integrated with the {HMD}\u2019s static environment mesh by actively removing human-generated artifacts. This integration is crucial, enabling physically plausible interactions between virtual entities and real users, supporting real-time collision detection, and ensuring correct occlusion handling where virtual content respects realworld spatial dynamics. Implemented entirely on the Magic Leap 2, our method achieves low-latency pose updates (under 40 ms) and full 3D lifting (under 60 ms). Comparative evaluation against the {RTMW}3D-x baseline shows a Procrustes-Aligned Mean Per Joint Position Error below 140 mm, with absolute depth placement validated using an external Azure Kinect sensor. Rhino-{AR} demonstrates the feasibility of robust, realtime human-aware perception on mobile {AR} platforms, enabling new classes of interactive, spatially-aware applications without external computation.<\/p>\n","protected":false},"excerpt":{"rendered":"
Real-time understanding of dynamic human presence is crucial for immersive Augmented Reality ({AR}), yet challenging on resource-constrained Head-Mounted Displays ({HMDs}). This paper introduces Rhino-{AR}, a pipeline for ondevice 3D human pose estimation and dynamic scene integration for commercial {AR} headsets like the Magic Leap 2. Our system processes {RGB} and sparse depth data, first detecting 2D keypoints, then robustly lifting them to 3D. Beyond pose estimation, we reconstruct a coarse […]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-36739","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/36739","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/36739\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=36739"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=36739"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}