  We introduce HUMAN4D, 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. Moreover, a spatio-temporally aligned scanned
and rigged 3D character complements HUMAN4D to enable joint research on
time-varying and high-quality dynamic meshes. We provide evaluation baselines
by benchmarking HUMAN4D with state-of-the-art human pose estimation and 3D
compression methods. For the former, we apply 2D and 3D pose estimation
algorithms both on single- and multi-view data cues. For the latter, we
benchmark open-source 3D codecs on volumetric data respecting online volumetric
video encoding and steady bit-rates. Furthermore, qualitative and quantitative
visual comparison between mesh-based volumetric data reconstructed in different
qualities showcases the available options with respect to 4D representations.
HUMAN4D is introduced to the computer vision and graphics research communities
to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD
and audio data cues. The dataset and its code are available
https://tofis.github.io/myurls/human4d.
