arXiv:2610.19494v1 Announce Type: new Abstract: Real-time monocular human pose estimation is an important sensing component for human-centered intelligent systems. While recent camera-based methods h...
This scoping review examined 117 studies on video-based markerless motion capture, most published from 2024 onward and focused on healthy adults walking in laboratories. The studies identified five main pipeline architectures, but most reported only raw joint angles without biomechanical refinement, achieving sagittal lower‑limb agreement of about 5–6°, which falls short of clinical acceptability. Validation of out‑of‑plane kinematics, kinetics, and performance in older or pathological populations was rare, and emerging computer‑vision techniques such as foundation‑model mesh recovery and differentiable inverse kinematics were largely absent from validated work.
arXiv:2609.22619v1 Announce Type: new Abstract: Tracking recovery of walking function requires detecting meaningful gait change across rehabilitation sessions, yet objective 3D measurement remains co...
arXiv:2609.18406v1 Announce Type: new Abstract: Recovering 3D human body motion from video is important for applications such as rehabilitation assessment and sports performance evaluation. For prost...
Recovering 3D human body motion from video is important for applications such as rehabilitation assessment and sports performance evaluation. For prosthesis users, this requires capturing both natural...
arXiv:2607. 08725v1 Announce Type: cross Abstract: Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable.
Radar-based human pose estimation has focused on improving learning algorithms while representing the body as unconstrained keypoint coordinates. We address the underexplored dimension of anatomical fidelity by integrating a full-body skeletal model into a differentiable, end-to-end trainable radar-based pose estimation framework, in which the pose network is supervised through forward kinematics while subject-specific geometry is fitted beforehand.
arXiv:2608.29928v1 Announce Type: new Abstract: State-of-the-art monocular body recovery methods predict mesh vertices and angles on the corresponding kinematic tree, but their outputs lack biomechan...
arXiv:2610.05552v2 Announce Type: replace Abstract: Three-dimensional gait analysis guides rehabilitation after spinal cord injury but depends on marker-based motion capture and force plates, which f...
arXiv:2606. 27918v1 Announce Type: cross Abstract: As a prominent symptom of Parkinson's disease (PD), turning impairment is evaluated through parameters such as turning angle, duration, and particularly, the number of steps required to complete a turn, which directly reflects motor dysfunction.
The study investigates how well a single consumer earbud IMU can estimate 3D body pose and whether adding foot IMUs improves accuracy. Using a multimodal capture pipeline with RGB‑D video, an AirPods head IMU, and Striv insole IMUs, the authors benchmark pose estimation across various motions and train recurrent models (IMUPoser and MobilePoser). Results show that a head IMU alone achieves 79.0 mm rigid‑MPJPE and 0.809 macro‑F1 for foot contact, while adding foot IMUs does not significantly improve pose and can even degrade performance due to insole orientation errors.
DirtyMoCap is a marker‑layout‑free framework that converts unordered, noisy optical motion capture markers into a fixed set of proxy anchors representing skeletal joints and body surface points. Using a recurrent sliding‑window architecture to track these anchors and a custom differentiable Gauss‑Newton solver to fit the SMPL‑H model, the method learns adaptive observation confidence, smoothness, and prior weights end‑to‑end. Experiments show that DirtyMoCap generalizes across arbitrary marker configurations, outperforms configuration‑specific baselines in joint and vertex accuracy, and achieves up to a 100× speedup over standard PyTorch implementations, enabling the creation of a temporally coherent Kung Fu motion dataset.