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Linear Object Classes and Image Synthesis from a Single Example Image

機(jī)譯：單個示例圖像的線性對象類和圖像合成

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The need to generate new views of a 3D object from a single real image arises inseveral fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, we have recently introduced techniques that are applicable under restricted conditions but simpler. The approach exploits image transformations that are specific to the relevant object class anc learnable from example views of other prototypical objects of the same class. In this paper, we introduce such a new technique by extending the notion of linear class first proposed by Poggio and Vetter. For linear object classes it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively rotate high-resolution face images from a single 2D view.

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