MoVi: A large multi-purpose human motion and video dataset

Abstract

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. 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. 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. 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. The processed motion capture data is also available as realistic 3D human meshes. We anticipate use of this dataset for research on human pose estimation, action recognition, motion modelling, gait analysis, and body shape reconstruction.

Probabilistic 3D Human Shape and Pose Estimation from Multiple Unconstrained Images in the Wild

“…However, video inputs allow these methods to enforce consistent body shapes and smooth motions across frames, e.g. using motion discriminators [11], optical flow [1,49], or texture consistency [40]. Learning-based video methods also overcome 3D data scarcity by incorporating weak 2D supervision [26,20], or with self-supervision enforcing visual consistency between frames [49,40,1].…”

SOMA: Solving Optical Marker-Based MoCap Automatically

“…Evaluation Datasets. We evaluate SOMA quantitatively on various mocap datasets with real marker data and synthetic noise; namely: BMLrub [54], BMLmovi [16], and KIT [32]. The individual datasets offer various maker layouts with different marker density, subject shape variation, body pose, and recording systems.…”

Imposing Temporal Consistency on Deep Monocular Body Shape and Pose Estimation

“…The second database consists of videos from MoVi [6], which were processed to image sequences. MoVi is a large marker-based motion capture dataset on videos with actors wearing natural or minimal clothing and performing various body motions.…”

“…The employed data set MoVi is available under [6], the data set own-data and the code produced for this paper are not publicly available. There are no competing interests and no further acknowledgements regarding this publication.…”