Abstract:
3D human body meshes are high demanded in the field of computer animation, gaming and TV industries. Although the techniques of creating 3D meshes have been improved a lot, the synthesis of 3D meshes is still non-trivial. Professional tools like Maya and Blender are the mainstream way to produce 3D meshes, which is time costing and needs expert knowledge of 3D model and animation.
With the rapid technology of 3D scanning, many works start to scan 3D from real people. Expensive scanners and complex equipment setup have been used to build high-quality 3D meshes, such as the datasets of SCAPE [1], MPI [2], FAUST [3] et. al. To the best of our knowledge, there is no publicly available dataset of human body meshes with different shapes and poses acquired with low-cost scanners, like Kinect. Compared with the high-quality meshes, noises, outliers and holes (missing data) are prevalent in their low-quality counterparts acquired with Kinect. These imperfections of data provide more challenges to some critical research problems like mesh registration, mesh modelling et. al.
We name the dataset as "K3D-hub" for Kinect based 3D human body dataset. This dataset currently consists of 50 subjects in 5 predefined poses. Some examples are shown below.

Note: If this data is used, in whole or in part, for any publishable work, please reference the following paper:

Zongyi Xu, Qianni Zhang, and Shiyang Cheng. "Multilevel active registration for kinect human body scans: from low quality to high quality." Multimedia Systems (2017): 1-14.

References:
[1] D. Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers, and J. Davis. SCAPE: Shape completion and animation of people. ACM Trans. Graph., 24(3):408-416, 2005.
[2] Hasler, Nils, Carsten Stoll, Martin Sunkel, Bodo Rosenhahn, and H-P. Seidel. A statistical model of human pose and body shape. In Computer Graphics Forum, vol. 28, no. 2, pp. 337-346. Blackwell Publishing Ltd, 2009.
[3] F. Bogo, J. Romero, M. Loper, and M. J. Black. FAUST: Dataset and evaluation for 3D mesh registration. CVPR, pp. 3794-3801, 2014.