4 citations · 5 across the 7 of their papers we have counts for
7 papers
Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capture
R. James Cotton
Recent developments have created differentiable physics simulators designed for machine learning pipelines that can be accelerated on a GPU. While these can simulate biomechanical…
Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulation
Nikolaos Smyrnakis, Tasos Karakostas, R. James Cotton
Gait analysis from videos obtained from a smartphone would open up many clinical opportunities for detecting and quantifying gait impairments. However, existing approaches for esti…
Generalization properties of contrastive world models
Kandan Ramakrishnan, R. James Cotton, Xaq Pitkow +1
Recent work on object-centric world models aim to factorize representations in terms of objects in a completely unsupervised or self-supervised manner. Such world models are hypoth…
Dynamic Gaussian Splatting from Markerless Motion Capture can Reconstruct Infants Movements
R. James Cotton, Colleen Peyton
Easy access to precise 3D tracking of movement could benefit many aspects of rehabilitation. A challenge to achieving this goal is that while there are many datasets and pretrained…
Self-Supervised Learning of Gait-Based Biomarkers
R. James Cotton, J. D. Peiffer, Kunal Shah +5
Markerless motion capture (MMC) is revolutionizing gait analysis in clinical settings by making it more accessible, raising the question of how to extract the most clinically meani…
Markerless Motion Capture and Biomechanical Analysis Pipeline
R. James Cotton, Allison DeLillo, Anthony Cimorelli +5
Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis. This could greatly benefit rehabili…