Results 251 to 260 of about 367,868 (278)

Validation of pitchAI ^TM markerless motion capture using marker-based 3D motion capture

Sports Biomechanics, 2022
This study sought to compare and validate baseball pitching mechanics, including joint angles and spatiotemporal parameters, from a single camera markerless motion capture solution with a 3D optical marker-based system. Ten healthy pitchers threw 2-3 maximum effort fastballs while concurrently using marker-based optical capture and pitchAITM ...
Tyler J. Dobos, Ryan W. G. Bench, Colin D. McKinnon, Anthony Brady, Kyle J. Boddy, Michael W. R. Holmes, Michael W. L. Sonne   +6 more
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3D tree modeling using motion capture

2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, 2012
Recovering tree shape from motion capture data is a first step toward efficient and accurate animation of trees in wind using motion capture data. Existing algorithms for generating models of tree branching structures for image synthesis in computer graphics are not adapted to the unique data set provided by motion capture. We present a method for tree
Jie Long, Michael D. Jones
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Markerless 3D facial motion capture system

SPIE Proceedings, 2012
We propose a novel markerless 3D facial motion capture system using only one common camera. This system is simple and easy to transfer facial expressions of a user's into virtual world. It has robustly tracking facial feature points associated with head movements. In addition, it estimates high accurate 3D points' locations.
Youngkyoo Hwang, Jung-Bae Kim, Xuetao Feng, Won-Chul Bang, Taehyun Rhee, James D.K. Kim, ChangYeong Kim   +6 more
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Gait Recognition from Markerless 3D Motion Capture

2019 International Conference on Biometrics (ICB), 2019
State of the art gait recognition methods often make use of the shape of the body as well as its movement, as observed in the use of Gait Energy Images(GEIs), for recognition. However, it is desirable to have a method that works exclusively with the movement of the body, as clothing and other factors may interfere with the biometric signature from body
James Rainey, John Bustard, Seán McLoone   +2 more
openaire   +1 more source

Multisensor-fusion for 3D full-body human motion capture

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010
In this work, we present an approach to fuse video with orientation data obtained from extended inertial sensors to improve and stabilize full-body human motion capture. Even though video data is a strong cue for motion analysis, tracking artifacts occur frequently due to ambiguities in the images, rapid motions, occlusions or noise. As a complementary
Pons-Moll, G., Baak, A., Helten, T., Müller, M., Seidel, H. ; https://orcid.org/0000-0002-1343-8613, Rosenhahn, B.   +5 more
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Learning-based 3D human motion capture and animation synthesis

2023
Realistische virtuelle menschliche Avatare sind ein entscheidendes Element in einer Vielzahl von Anwendungen, von 3D-Animationsfilmen bis hin zu neuen AR/VR-Technologien. Die Erzeugung glaubwürdiger Bewegungen solcher Avatare in drei Dimensionen ist bekanntermaßen eine herausfordernde Aufgabe.
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Use of Motion Capture in 3D Animation: Motion Capture Systems, Challenges, and Recent Trends

2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019
In today’s world capturing of motion from a video stream is a growing and challenging area. Motion capture system (Mocap) is widely used in today’s realistic world including sports, entertainment, video gaming and many more. In this paper, firstly we provide an overview of different types of Mocap on the basis of Marker-based Mocap and Markerless Mocap
Shubham Sharma, Shubhankar Verma, Mohit Kumar, Lavanya Sharma   +3 more
openaire   +1 more source