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ZENODO
Dataset . 2021
Data sources: Datacite
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Dataset . 2021
Data sources: Datacite
ZENODO
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HUMAN4D - Subject #4 (multi-RGBD + 2d/3d pose)

Authors: Chatzitofis, Anargyros;

HUMAN4D - Subject #4 (multi-RGBD + 2d/3d pose)

Abstract

HUMAN4D: A Human-Centric Multimodal Dataset for Motions & Immersive Media (Subject #4) The dataset was captured with the use of VCL Volumetric Capture free software (https://github.com/VCL3D/VolumetricCapture) device_repository.json includes the camera instrinsic parameters. pose.zip includes the camera extrinsic calibration parameters. offsets.zip include the frame offset between the pose ids (name_of_file==id) and the group frame ids of the RGBD data (first number before underscore in the filename of each file) S4_activities.txt files that maps the zip filenames with data for specific activities. HUMAN4D is a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC.

Country
European Union
Keywords

depth, multi-view, motion capture, rgbd, pose estimation

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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