Semantic Parametric Reshaping of Human Body Models
Yipin Yang, Yao Yu, Yu Zhou, Sidan Du, James Davis, Ruigang Yang
We develop a novel approach to generate human body models in a variety of shapes and poses via tuning semantic
parameters. Our approach is investigated with datasets of up to 3000 scanned body models which have been placed
in point to point correspondence. Correspondence is established by nonrigid deformation of a template mesh. The
large dataset allows a local model to be learned robustly, in which individual parts of the human body can be
accurately reshaped according to semantic parameters. We evaluate performance on two datasets and find that our
model outperforms existing methods.
If you use this dataset, please cite the following paper:
The dataset contains about 1500 registered male and female meshes with point-to-point correspondences respectively.
Each mesh has 12500 vertices and 25000 facets.
The data is derived from the CAESAR dataset. I was given permission to share research results, but not original data. Thus there are no raw scans or weights, heights, etc available with our meshes.
No commercial usage of the data is allowed.
Click here to obtain a username and password for dataset access.