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We observe that conventional static clutter removal not only suppresses environmental reflections but also erases the body's involuntary micro-motions, such as breathing, heartbeat, and subtle sway. Our key insight is that these micro-motions are not noise but information, encoding physiological vitality even in apparent stillness. Realizing this shift\u2014from detecting motion to perceiving life within stillness\u2014introduces two fundamental challenges:\n                    <jats:bold>(C1)<\/jats:bold>\n                    the echoes of these micro-motions are orders of magnitude weaker than static clutter, spectrally overlap within the near-zero Doppler region and spatially co-located with dominant reflections; and\n                    <jats:bold>(C2)<\/jats:bold>\n                    mmWave reflections are inherently sparse and geometry-agnostic, lacking the structural priors required to recover body's shape and pose across users and environments. To address these, we design mmRehab, a transformative mmWave sensing system for rehabilitation, extending radar perception beyond motion to enable physiological interpretation even when users remain still. Within mmRehab,\n                    <jats:italic toggle=\"yes\">Micro-motion Feature Extraction<\/jats:italic>\n                    addresses\n                    <jats:bold>C1<\/jats:bold>\n                    through beamforming-based spatial isolation and micro-Doppler temporal discrimination, amplifying respiration- and posture-related cues;\n                    <jats:italic toggle=\"yes\">Geometry-aware Knowledge Transfer<\/jats:italic>\n                    addresses\n                    <jats:bold>C2<\/jats:bold>\n                    via depth-guided distillation, transferring structural priors from vision to radar representations for robust generalization. Extensive experiments on both dynamic and static rehabilitation tasks show that mmRehab reduces 3D reconstruction errors by over 24% and generalizes robustly to unseen users, distances, and orientations\u2014demonstrating the feasibility of unified radar perception for motion and micro-motion rehabilitation monitoring.\n                  <\/jats:p>","DOI":"10.1145\/3790117","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T17:51:14Z","timestamp":1773683474000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Sensing Life in Stillness: Unified Dynamic and Static Human Mesh Reconstruction with mmWave Radar"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5899-7697","authenticated-orcid":false,"given":"Lin","family":"Chen","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8602-4203","authenticated-orcid":false,"given":"Cong","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1758-2870","authenticated-orcid":false,"given":"Shuxin","family":"Zhong","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7758-4956","authenticated-orcid":false,"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9735-8183","authenticated-orcid":false,"given":"Yufei","family":"Wen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9471-3101","authenticated-orcid":false,"given":"Haotian","family":"Song","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2216-0737","authenticated-orcid":false,"given":"Kaishun","family":"Wu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818072"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICoBE.2012.6179051"},{"key":"e_1_2_1_3_1","volume-title":"mri: Multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors. 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