We describe how to learn a compact and efficient model of the surface deformation of human hands. The model is built from a set of noisy depth images of a diverse set of subjects peiforming dif f erent poses with their hands. We represent the observed suiface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model. The model simultaneously accounts for variation in subject-specific shape and subject-agnostic pose. Specifically, hand shape is parameterized as a linear combination of a mean mesh in a neutral pose with a small number of offset vectors. This mesh is then articulated using standard linear blend skinning (LBS) to generate the con trol mesh of a subdivision suiface. We define an energy that encourages each depth pixel to be explained by our model, and the use of a smooth subdivision suiface allows us to op timize for all parameters jointly from a rough initialization. The efficacy of our method is demonstrated using both syn thetic and real data, where it is shown that hand shape vari ation can be represented using only a small number of basis components. We compare with other approaches including peA and show a substantial improvement in the representa tional power of our model, while maintaining the efficiency of a linear shape basis.
REF go further to learn a model of shape variation from scans of 50 people captured with a depth sensor.
Learning an efficient model of hand shape variation from depth images
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science