Papers Markerless Motion Capture
“Markerless Motion Capture” 태그가 달린 논문 38편 · 필터 해제
Fast Neural Inverse Kinematics on Human Body Motions
Markerless motion capture enables the tracking of human motion without requiring physical markers or suits, offering increased flexibility and reduced costs compared to traditional systems. However, these advantages ofte…
Markerless Motion CaptureMAMMA: Markerless & Automatic Multi-Person Motion Action Capture
We present MAMMA, a markerless motion-capture pipeline that accurately recovers SMPL-X parameters from multi-view video of two-person interaction sequences. Traditional motion-capture systems rely on physical markers. Al…
Markerless Motion CaptureBiomechGPT: Towards a Biomechanically Fluent Multimodal Foundation Model for Clinically Relevant Motion Tasks
Advances in markerless motion capture are expanding access to biomechanical movement analysis, making it feasible to obtain high-quality movement data from outpatient clinics, inpatient hospitals, therapy, and even home.…
Activity RecognitionDescriptiveLanguage ModelingLanguage Modelling+1KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture
Broader access to high-quality movement analysis could greatly benefit movement science and rehabilitation, such as allowing more detailed characterization of movement impairments and responses to interventions, or even …
Imitation LearningMarkerless Motion CapturePose EstimationBiomechanical Reconstruction with Confidence Intervals from Multiview Markerless Motion Capture
Advances in multiview markerless motion capture (MMMC) promise high-quality movement analysis for clinical practice and research. While prior validation studies show MMMC performs well on average, they do not provide wha…
Markerless Motion CaptureDifferentiable Biomechanics for Markerless Motion Capture in Upper Limb Stroke Rehabilitation: A Comparison with Optical Motion Capture
Marker-based Optical Motion Capture (OMC) paired with biomechanical modeling is currently considered the most precise and accurate method for measuring human movement kinematics. However, combining differentiable biomech…
Markerless Motion CaptureVideoRun2D: Cost-Effective Markerless Motion Capture for Sprint Biomechanics
Sprinting is a determinant ability, especially in team sports. The kinematics of the sprint have been studied in the past using different methods specially developed considering human biomechanics and, among those method…
Markerless Motion Capture3D Pose-Based Temporal Action Segmentation for Figure Skating: A Fine-Grained and Jump Procedure-Aware Annotation Approach
Understanding human actions from videos is essential in many domains, including sports. In figure skating, technical judgments are performed by watching skaters' 3D movements, and its part of the judging procedure can be…
Action SegmentationMarkerless Motion CaptureTemporal Action SegmentationOpenCap markerless motion capture estimation of lower extremity kinematics and dynamics in cycling
Markerless motion capture offers several benefits over traditional marker-based systems by eliminating the need for physical markers, which are prone to misplacement and artifacts. Utilizing computer vision and deep lear…
Markerless Motion CaptureFusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics
Video and wearable sensor data provide complementary information about human movement. Video provides a holistic understanding of the entire body in the world while wearable sensors provide high-resolution measurements o…
Markerless Motion CaptureSensor FusionSkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers
We introduce SkelFormer, a novel markerless motion capture pipeline for multi-view human pose and shape estimation. Our method first uses off-the-shelf 2D keypoint estimators, pre-trained on large-scale in-the-wild data,…
3D Human Pose EstimationKeypoint DetectionMarkerless Motion CaptureMulti-view 3D Human Pose EstimationDifferentiable 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. While these can simulate biomechanical models, these opportunities have not bee…
GPUMarkerless Motion Capture3D 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 load…
Keypoint DetectionMarkerless Motion CaptureMachine 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) du…
Markerless Motion CaptureBundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos
Capturing smooth motions from videos using markerless techniques typically involves complex processes such as temporal constraints, multiple stages with data-driven regression and optimization, and bundle solving over te…
3D human pose and shape estimation3D Human Pose EstimationMarkerless Motion CaptureDynamic Gaussian Splatting from Markerless Motion Capture can Reconstruct Infants Movements
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 algorithms for able-bodied adults, algo…
Markerless Motion CaptureSemantic SegmentationSelf-Supervised Learning of Gait-Based Biomarkers
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 meaningful information from gait data. In mul…
Contrastive LearningDiagnosticMarkerless Motion CaptureSelf-Supervised LearningMarkerless 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. This could greatly benefit rehabilitation by enabling more accurate trackin…
Markerless Motion CapturePose EstimationQuantifying Jump Height Using Markerless Motion Capture with a Single Smartphone
Goal: The countermovement jump (CMJ) is commonly used to measure lower-body explosive power. This study evaluates how accurately markerless motion capture (MMC) with a single smartphone can measure bilateral and unilater…
Camera CalibrationMarkerless Motion CaptureTowards Single Camera Human 3D-Kinematics
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 performi…
Markerless Motion Capture