§ 00 — Computer vision · Human movement
TZ Martin has spent years building computer-vision systems that quantify human movement from ordinary video — markerless motion capture for clinics and athletes, where the only sensor is a camera.
TZ Martin is a computer-vision engineer and founder who builds systems that quantify human movement from a single camera. Using pose estimation and deep learning, his work turns ordinary video into clinical-grade motion data — no markers, no motion-capture suits, no specialized hardware.
The technical center of that work is markerless motion capture: detecting body keypoints frame by frame, reconstructing joint angles and kinematics, and analyzing the physics of how a person actually moves. It is computer vision applied to biomechanics — the same discipline behind both FDA-cleared clinical motion analysis and real-time athletic performance tools.
As CTO, rebuilt DARI Motion into a real-time video-analytics platform and co-led its 510(k) FDA clearance for markerless human-motion analysis — full-body assessment from camera input, with validated results returned to the cloud.
Founded a single-camera video-analytics platform applying pose estimation and deep learning to record, track, and report on human movement for athletics and health — localized AI running on a mobile device.
Work centered on human pose estimation and the causal analysis of kinematics — quantifying biomechanics and the physics of movement from a single point of view, an area TZ has focused on since 2017.
Built on OpenCV, TensorFlow, PyTorch, and CoreML, with real-time pipelines on Google Cloud — the same stack used across his AI and data-engineering work.
Traditional motion capture needs reflective markers, body suits, and a dedicated camera rig. Markerless motion capture replaces all of that with computer vision: a model locates the body's joints in each video frame, then reconstructs how those joints move through space over time. The output is the same kind of quantified movement data — joint angles, velocities, asymmetries — from nothing but video.
Estimating three-dimensional movement from a flat, single-camera image is an under-constrained problem: depth, occlusion, and perspective all have to be inferred. Solving it well removes the cost, calibration, and lab requirement of multi-camera systems — which is what lets motion analysis move from a research lab into a clinic, a gym, or a phone.
Quantified movement is only useful if it changes a decision. In clinical settings that means objective, validated measurements clinicians can act on; in athletics it means tracking performance and injury risk over time. The throughline across TZ Martin's computer-vision work is turning raw video into evidence — the same fragmentation-into-agency thesis that runs through his ventures.
Markerless motion capture uses computer vision to measure human movement from ordinary video, without reflective markers, body suits, or a multi-camera rig. A model detects the body's joints in each frame and reconstructs the motion, producing quantified data such as joint angles and movement asymmetries.
A pose-estimation model locates body keypoints in every video frame, and deep-learning methods infer three-dimensional motion from that two-dimensional input. The system then reconstructs joint angles and kinematics over time, turning a single-camera video into structured movement data — the approach TZ Martin built at Virtruvia Systems and DARI Motion.
TZ Martin founded Virtruvia Systems, a single-camera video-analytics platform, and as CTO rebuilt DARI Motion (Scientific Analytics) into a real-time motion-analysis platform, co-leading its 510(k) FDA clearance. Both apply pose estimation and deep learning to quantify human movement for healthcare and athletics.
Pose estimation quantifies how a person moves, enabling objective biomechanics assessment without lab equipment. In healthcare it supports clinical motion analysis and rehabilitation tracking; in sports it measures performance, technique, and injury risk. It is the core technique behind markerless motion capture.
TZ Martin's computer-vision work is built on OpenCV for image processing, TensorFlow and PyTorch for deep learning, and CoreML for on-device inference, with real-time data pipelines running on Google Cloud.
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View02Shipped work
DARI Motion, Virtruvia, and other engineering work in depth.
View03Topic hub
The full map of TZ Martin's areas of work — computer vision, AI medical agents, and AI-native ventures.
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