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Found 8 papers, 6 papers with code
WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion
We address these limitations with WHAM (World-grounded Humans with Accurate Motion), which accurately and efficiently reconstructs 3D human motion in a global coordinate system from video.
Ranked #1 on
3D Human Pose Estimation
on EMDB
Markerless Motion Tracking with Noisy Video and IMU Data
We propose deep learning models to estimate human movement with noisy data from videos (VideoNet), inertial sensors (IMUNet), and a combination of the two (FusionNet), obviating the need for careful calibration.
SRL-Assisted AFM: Generating Planar Unstructured Quadrilateral Meshes with Supervised and Reinforcement Learning-Assisted Advancing Front Method
These deep neural networks are trained using a unique pipeline that combines supervised learning with reinforcement learning to iteratively improve mesh quality.
Predicting knee adduction moment response to gait retraining with minimal clinical data
Insights learned from a ground-truth dataset with both baseline and toe-in gait trials (N = 12) enabled the creation of a large (N = 138) synthetic dataset for training the predictive model.
Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework
Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings.
Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning
Subregional T2 values and four-year changes were calculated using a musculoskeletal radiologist's segmentations (Reader 1) and the model's segmentations.
Multi-view Human Pose and Shape Estimation Using Learnable Volumetric Aggregation
In this paper, we propose a learnable volumetric aggregation approach to reconstruct 3D human body pose and shape from calibrated multi-view images.
ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information
In healthcare applications, temporal variables that encode movement, health status and longitudinal patient evolution are often accompanied by rich structured information such as demographics, diagnostics and medical exam data.
