Markerless Motion Capture
8 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Markerless Motion Capture
RICH
Latest papers with no code
SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers
Next, we design a regression-based inverse-kinematic skeletal transformer that maps the joint positions to pose and shape representations from heavily noisy observations.
Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capture
Recent developments have created differentiable physics simulators designed for machine learning pipelines that can be accelerated on a GPU.
3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data
Accurate 3D kinematics estimation of human body is crucial in various applications for human health and mobility, such as rehabilitation, injury prevention, and diagnosis, as it helps to understand the biomechanical loading experienced during movement.
BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos
It solves the motion capture task in a single stage, eliminating the need for temporal smoothness objectives while still delivering smooth motions.
Dynamic Gaussian Splatting from Markerless Motion Capture can Reconstruct Infants Movements
This work paves the way for advanced movement analysis tools that can be applied to diverse clinical populations, with a particular emphasis on early detection in infants.
Self-Supervised Learning of Gait-Based Biomarkers
We find that contrastive learning on unannotated gait data learns a representation that captures clinically meaningful information.
Markerless Motion Capture and Biomechanical Analysis Pipeline
Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis.
Quantifying Jump Height Using Markerless Motion Capture with a Single Smartphone
This study evaluates how accurately markerless motion capture (MMC) with a single smartphone can measure bilateral and unilateral CMJ jump height.
Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations
A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior.
Measuring and modeling the motor system with machine learning
The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data.
