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| April 24
| April 24
| ''post ICCV deadline, no meeting''
| Sameh Khamis
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| Learning an Efficient Model of Hand Shape Variation from Depth Images
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| May 1
| May 1
Related paper:
Related paper:
Matrix Completion for Resolving Label Ambiguity, To appear in CVPR 2015
Matrix Completion for Resolving Label Ambiguity, To appear in CVPR 2015
===Learning an Efficient Model of Hand Shape Variation from Depth Images===
Speaker: Sameh Khamis
Abstract: 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 performing different poses with their hands. We represent the observed surface 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 control mesh of a subdivision surface. We define an energy that encourages each depth pixel to be explained by our model, and the use of a smooth subdivision surface allows us to optimize for all parameters jointly from a rough initialization. The efficacy of our method is demonstrated using both synthetic and real data, where it is shown that hand shape variation can be represented using only a small number of basis components. We compare with other approaches including PCA and show a substantial improvement in the representational power of our model, while maintaining the efficiency of a linear shape basis.
Link: Learning an Efficient Model of Hand Shape Variation from Depth Images (To appear in CVPR 2015)
===Beyond Short Snippets: Deep Networks for Video Classification===
===Beyond Short Snippets: Deep Networks for Video Classification===
