Markerless Motion Capture
10 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.
3D Pose-Based Temporal Action Segmentation for Figure Skating: A Fine-Grained and Jump Procedure-Aware Annotation Approach
In the experimental results, we validated the usefulness of 3D pose features as input and the fine-grained dataset for the TAS model in figure skating.
VideoRun2D: Cost-Effective Markerless Motion Capture for Sprint Biomechanics
This investigation first adapts two of these general trackers (MoveNet and CoTracker) for realistic biomechanical analysis and then evaluate them in comparison to manual tracking (with key points manually marked using the software Kinovea).
