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We propose a method for learning shape models enabling accurate articulated human pose estimation from a single image. Where previous work has typically employed simple geometric models of human limbs e.g. cylinders which lead to rectangular projections, we propose to learn a generative model of limb shape which can capture the wide variation in shape due to varying anatomy and pose. The model is learnt from silhouette, depth and 3D pose data provided by a Microsoft Xbox Kinect, such that no manual annotation is required. We employ the learnt model in a pictorial structure model framework and demonstrate improved pose estimation from single silhouettes compared to using conventional rectangular limb models.