  4D human sensing and modeling are fundamental tasks in vision and graphics
with numerous applications. With the advances of new sensors and algorithms,
there is an increasing demand for more versatile datasets. In this work, we
contribute HuMMan, a large-scale multi-modal 4D human dataset with 1000 human
subjects, 400k sequences and 60M frames. HuMMan has several appealing
properties: 1) multi-modal data and annotations including color images, point
clouds, keypoints, SMPL parameters, and textured meshes; 2) popular mobile
device is included in the sensor suite; 3) a set of 500 actions, designed to
cover fundamental movements; 4) multiple tasks such as action recognition, pose
estimation, parametric human recovery, and textured mesh reconstruction are
supported and evaluated. Extensive experiments on HuMMan voice the need for
further study on challenges such as fine-grained action recognition, dynamic
human mesh reconstruction, point cloud-based parametric human recovery, and
cross-device domain gaps.
