{"ID":2832175,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06275","arxiv_id":"2512.06275","title":"FacePhys: State of the Heart Learning","abstract":"Vital sign measurement using cameras presents opportunities for comfortable, ubiquitous health monitoring. Remote photoplethysmography (rPPG), a foundational technology, enables cardiac measurement through minute changes in light reflected from the skin. However, practical deployment is limited by the computational constraints of performing analysis on front-end devices and the accuracy degradation of transmitting data through compressive channels that reduce signal quality. We propose a memory efficient rPPG algorithm - \\emph{FacePhys} - built on temporal-spatial state space duality, which resolves the trilemma of model scalability, cross-dataset generalization, and real-time operation. Leveraging a transferable heart state, FacePhys captures subtle periodic variations across video frames while maintaining a minimal computational overhead, enabling training on extended video sequences and supporting low-latency inference. FacePhys establishes a new state-of-the-art, with a substantial 49\\% reduction in error. Our solution enables real-time inference with a memory footprint of 3.6 MB and per-frame latency of 9.46 ms -- surpassing existing methods by 83\\% to 99\\%. These results translate into reliable real-time performance in practical deployments, and a live demo is available at https://www.facephys.com/.","short_abstract":"Vital sign measurement using cameras presents opportunities for comfortable, ubiquitous health monitoring. Remote photoplethysmography (rPPG), a foundational technology, enables cardiac measurement through minute changes in light reflected from the skin. However, practical deployment is limited by the computational con...","url_abs":"https://arxiv.org/abs/2512.06275","url_pdf":"https://arxiv.org/pdf/2512.06275v1","authors":"[\"Kegang Wang\",\"Jiankai Tang\",\"Yuntao Wang\",\"Xin Liu\",\"Yuxuan Fan\",\"Jiatong Ji\",\"Yuanchun Shi\",\"Daniel McDuff\"]","published":"2025-12-06T03:54:12Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","project_urls":"[\"https://www.facephys.com/\"]","has_code":false}