Markerless Motion Capture
8 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Markerless Motion Capture
RICH
Most implemented papers
Capturing and Inferring Dense Full-Body Human-Scene Contact
We capture a new dataset called RICH for "Real scenes, Interaction, Contact and Humans."
Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture
MatteoT90/WibergianLearning
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We propose a CNN-based approach for multi-camera markerless motion capture of the human body.
Synergetic Reconstruction from 2D Pose and 3D Motion for Wide-Space Multi-Person Video Motion Capture in the Wild
We evaluated the proposed method using various datasets and a real sports field.
SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos
In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input.
A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets
Markerless motion capture has become an active field of research in computer vision in recent years.
PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images
To address these issues, we propose a Pyramidal Mesh Alignment Feedback (PyMAF) loop in our regression network for well-aligned human mesh recovery and extend it as PyMAF-X for the recovery of expressive full-body models.
Towards Single Camera Human 3D-Kinematics
bittnerma/direct3dkinematicestimation
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Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos.
Machine Vision-Enabled Sports Performance Analysis
$\textbf{Goal:}$ This study investigates the feasibility of monocular 2D markerless motion capture (MMC) using a single smartphone to measure jump height, velocity, flight time, contact time, and range of motion (ROM) during motor tasks.
