arXiv subjects

Mohamed Adjel

Publications and source records attributed to Mohamed Adjel.

3 recordsLinked to original sources

arXiv2026

Rhythmic Fitness Beat 'Em All using Real-Time Monocular Human Pose Estimation

Short and frequent physical activity exercices mitigate the health risks of sedentary behavior. Yet, the lack of motivation remains a critical barrier to adoption. Exergames gamify physical activity, but most rely on dedicated hardware, and outside dance and rhythm games they rarely use the musical beat. We present a work-in-progress beat 'em up exergame played with the whole body in front of a single smartphone, which runs the entire pipeline: an off-the-shelf real-time 3D human mesh recovery model on the phone's neural processing unit, gesture recognition, and the game, shown on any TV through a USB-C to HDMI cable. Our contributions are (1) a 3D recognition of five shadow-boxing moves (guard, punch, slip, squat, jump), each defined by one metric measure in a body frame aligned with gravity and one threshold with hysteresis, personalised during a tutorial; (2) a rhythm stage whose discrete cues are placed on the song's kick drum and judged on the movement's capture time, with a delay learned for each player; and (3) a phone-only architecture, running at 30 frames per second, that turns a phone and an HDMI cable into an exergame console.

cs.HC↗

arXiv2025

PriorFormer: A Transformer for Real-time Monocular 3D Human Pose Estimation with Versatile Geometric Priors

This paper proposes a new lightweight Transformer-based lifter that maps short sequences of human 2D joint positions to 3D poses using a single camera. The proposed model takes as input geometric priors including segment lengths and camera intrinsics and is designed to operate in both calibrated and uncalibrated settings. To this end, a masking mechanism enables the model to ignore missing priors during training and inference. This yields a single versatile network that can adapt to different deployment scenarios, from fully calibrated lab environments to in-the-wild monocular videos without calibration. The model was trained using 3D keypoints from AMASS dataset with corresponding 2D synthetic data generated by sampling random camera poses and intrinsics. It was then compared to an expert model trained, only on complete priors, and the validation was done by conducting an ablation study. Results show that both, camera and segment length priors, improve performance and that the versatile model outperforms the expert, even when all priors are available, and maintains high accuracy when priors are missing. Overall the average 3D joint center positions estimation accuracy was as low as 36mm improving state of the art by half a centimeter and at a much lower computational cost. Indeed, the proposed model runs in 380$μ$s on GPU and 1800$μ$s on CPU, making it suitable for deployment on embedded platforms and low-power devices.

cs.CV↗

arXiv2025

Toward a Real-Time Framework for Accurate Monocular 3D Human Pose Estimation with Geometric Priors

Monocular 3D human pose estimation remains a challenging and ill-posed problem, particularly in real-time settings and unconstrained environments. While direct imageto-3D approaches require large annotated datasets and heavy models, 2D-to-3D lifting offers a more lightweight and flexible alternative-especially when enhanced with prior knowledge. In this work, we propose a framework that combines real-time 2D keypoint detection with geometry-aware 2D-to-3D lifting, explicitly leveraging known camera intrinsics and subject-specific anatomical priors. Our approach builds on recent advances in self-calibration and biomechanically-constrained inverse kinematics to generate large-scale, plausible 2D-3D training pairs from MoCap and synthetic datasets. We discuss how these ingredients can enable fast, personalized, and accurate 3D pose estimation from monocular images without requiring specialized hardware. This proposal aims to foster discussion on bridging data-driven learning and model-based priors to improve accuracy, interpretability, and deployability of 3D human motion capture on edge devices in the wild.

cs.CV↗