In the proposal, we tackle the novel visual recognition problem of 3D (three-dimensional) deformable object shape identities or categories. Images of 3D objects undergo large appearance changes due to different object poses (articulation or deformation) as well as camera view-points. We attempt to recognise objects from single images by their 3D shape identities (or intrinsic shapes) regardless of their present poses and camera view-points. Humans can perceive 3D shapes of objects from single images, provided that they have previously seen 3D shapes of similar other objects. The knowledge formerly learnt on 3D shapes is called 3D shape prior. A key idea for fulfilling the proposed task is to learn and exploit the shape priors for object recognition. The proposed research is well-lined with and goes beyond important topics of computer vision. Whereas much work for view-point invariant object recognition is limited to rigid object classes with bountiful textures, we consider deformable object shapes. In a series of work in the field of single view reconstruction, promising results have been shown for human body shape reconstruction under pose variations. There has also been a notable latest success in 3D human pose recognition. On the top of these results, we go beyond to capture 3D intrinsic shape variations for object recognition. The intended outcomes would benefit the relevant academic fields and their existing markets, and would also lead to potential new applications such as automatic monitoring of public obesity and animal tracking.