Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Cardboard People: A Parameterized Model of Articulated Image Motion
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Clothed People Detection in Still Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Representation and Detection of Deformable Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
SCAPE: shape completion and animation of people
ACM SIGGRAPH 2005 Papers
A system for articulated tracking incorporating a clothing model
Machine Vision and Applications
Capturing and animating occluded cloth
ACM SIGGRAPH 2007 papers
The Naked Truth: Estimating Body Shape Under Clothing
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Technical Section: Estimating body shape of dressed humans
Computers and Graphics
An efficient garment visual search based on shape context
WSEAS Transactions on Computers
A geometric approach to robotic laundry folding
International Journal of Robotics Research
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Creating Picture Legends for Group Photos
Computer Graphics Forum
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Detection, tracking, segmentation and pose estimation of people in monocular images are widely studied. Two-dimensional models of the human body are extensively used, however, they are typically fairly crude, representing the body either as a rough outline or in terms of articulated geometric primitives. We describe a new 2D model of the human body contour that combines an underlying naked body with a low-dimensional clothing model. The naked body is represented as a Contour Person that can take on a wide variety of poses and body shapes. Clothing is represented as a deformation from the underlying body contour. This deformation is learned from training examples using principal component analysis to produce eigen clothing. We find that the statistics of clothing deformations are skewed and we model the a priori probability of these deformations using a Beta distribution. The resulting generative model captures realistic human forms in monocular images and is used to infer 2D body shape and pose under clothing. We also use the coefficients of the eigen clothing to recognize different categories of clothing on dressed people. The method is evaluated quantitatively on synthetic and real images and achieves better accuracy than previous methods for estimating body shape under clothing.