On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
The space of human body shapes: reconstruction and parameterization from range scans
ACM SIGGRAPH 2003 Papers
Mesh editing with poisson-based gradient field manipulation
ACM SIGGRAPH 2004 Papers
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
SCAPE: shape completion and animation of people
ACM SIGGRAPH 2005 Papers
Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Analysis of segmented human body scans
GI '07 Proceedings of Graphics Interface 2007
Global Medical Shape Analysis Using the Volumetric Laplace Spectrum
CW '07 Proceedings of the 2007 International Conference on Cyberworlds
ACM SIGGRAPH 2009 papers
Technical Section: Estimating body shape of dressed humans
Computers and Graphics
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Multilinear (tensor) ICA and dimensionality reduction
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
The caesar project: a 3-D surface anthropometry survey
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Template-Based 3D Model Fitting Using Dual-Domain Relaxation
IEEE Transactions on Visualization and Computer Graphics
Map-based exploration of intrinsic shape differences and variability
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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Statistical shape analysis is a tool that allows to quantify the shape variability of a population of shapes. Traditional tools to perform statistical shape analysis compute variations that reflect both shape and posture changes simultaneously. In many applications, such as ergonomic design applications, we are only interested in shape variations. With traditional tools, it is not straightforward to separate shape and posture variations. To overcome this problem, we propose an approach to perform statistical shape analysis in a posture-invariant way. The approach is based on a local representation that is obtained using the Laplace operator.