In order to obtain body pose and kinematics at a resolution that is fine enough to make inferences about identity, action, and particularly stylistic features, we need large, high-quality datasets that can be used in both generative and discriminative contexts. The full information about the data is available on the dataset website ( ).įunding: This research was funded by a NSERC Discovery Grant and contributions from CFREF VISTA to NFT.Ĭompeting interests: The authors have declared that no competing interests exist.Ĭapturing, modelling, and simulating human body shape and kinematics has been an area of intense study in the fields of biomechanics, computer vision, and computer graphics, with applications including human-machine interactions, assistive healthcare, clinical diagnostics, and realistic computer animation pipelines. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The data underlying the results presented in the study are available from Scholars Portal Dataverse (DOI: 10.5683/SP2/JRHDRN). Received: MaAccepted: Published: June 17, 2021Ĭopyright: © 2021 Ghorbani et al. (2021) MoVi: A large multi-purpose human motion and video dataset. We anticipate use of this dataset for research on human pose estimation, action recognition, motion modelling, gait analysis, and body shape reconstruction.Ĭitation: Ghorbani S, Mahdaviani K, Thaler A, Kording K, Cook DJ, Blohm G, et al. The processed motion capture data is also available as realistic 3D human meshes. This multimodal dataset contains 9 hours of optical motion capture data, 17 hours of video data from 4 different points of view recorded by stationary and hand-held cameras, and 6.6 hours of inertial measurement units data recorded from 60 female and 30 male actors performing a collection of 21 everyday actions and sports movements. We address this issue in our dataset by using different hardware systems to record partially overlapping information and synchronized data that lend themselves to transfer learning. Creating datasets that combine naturalistic recordings with high-accuracy data about ground truth body shape and pose is challenging because different motion recording systems are either optimized for one or the other.
Large high-quality datasets of human body shape and kinematics lay the foundation for modelling and simulation approaches in computer vision, computer graphics, and biomechanics.
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