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Found 13 papers, 4 papers with code
Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation
In this study we focus on the new task of multi-hypothesis motion estimation.
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.
Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulation
Gait analysis from videos obtained from a smartphone would open up many clinical opportunities for detecting and quantifying gait impairments.
Generalization properties of contrastive world models
We systematically test the model under a number of different OOD generalization scenarios such as extrapolation to new object attributes, introducing new conjunctions or new attributes.
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.
Improved Trajectory Reconstruction for Markerless Pose Estimation
Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views, and has the potential to make movement analysis easy and quick, including gait analysis.
Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions
We evaluate cGNF on the Human~3. 6M dataset and show that cGNF provides a well-calibrated distribution estimate while being close to state-of-the-art in terms of overall minMPJPE.
Density Estimation
Multi-Hypotheses 3D Human Pose Estimation
Transforming Gait: Video-Based Spatiotemporal Gait Analysis
Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation.
PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research
We also highlight limitations of these algorithms when applied to clinical populations in a rehabilitation setting.
Spatiotemporal Characterization of Gait from Monocular Videos with Transformers
Using more than 9000 monocular video from an instrumented gait analysis lab, we evaluated the performance of existing algorithms for measuring kinematics.
Factorized Neural Processes for Neural Processes: $K$-Shot Prediction of Neural Responses
We overcome this limitation by formulating the problem as $K$-shot prediction to directly infer a neuron's tuning function from a small set of stimulus-response pairs using a Neural Process.
