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3 results for “3D motion capture”
Evaluating 3D Human Motion Capture using Apple ARKit against the Vicon System: A Dataset
<p><strong>A journal paper which was published in Applied Sciences gives detailed information about the dataset.</strong></p> <p>Reimer, L.M.; Kapsecker, M.; Fukushima, T.; Jonas, S.M. Evaluating 3D Human Motion Capture on Mobile Devices. Appl. Sci. <em>(2022)</em></p> <p><a href="https://www.mdpi.com/2076-3417/12/10/4806">https://www.mdpi.com/2076-3417/12/10/4806</a></p> <p>Please cite the corresponding paper when using the dataset.</p> <p> </p> <p>A dataset containing anonymized exercise data for eight exercises from ten subject. The exercise data was recorded with two iPads 11" (2021 version, Apple Inc., Cupertino, CA, USA) and a Vicon system. The two iPads were positioned frontal and in a 30° angle to the left side of the subject. The Vicon system used 14 cameras and captured the motion using the Full-body Plug-in-gait model.</p> <p>The dataset contains 220 files, 22 per subject. The structure of the dataset contains 10 folders, one per subject. Each folder contains two subfolders: ARKit and Vicon. Each ARKit folder holds two CSV files. Each Vicon folder holds 16 files, two per exercise: a .csv file with the motion data and a .xcp file containing meta data about the recording, including the camera setup and start/stop timestamps.</p> <p>Du to export problems, the ARKit files for the Side View do not always contain all joint data. The upper body joints are only available for three out of the ten subjects for the Side View.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 8. 3D model of some facial expressions
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 7. Avatar 3D model generation
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.