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Generative Models for Shape and Appearance

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I will present recent work from SIGGRAPH and CVPR . The first builds a generative model of fonts in an automatic and unsupervised learning process; the only input is a collection of existing font files and the output is a probabilistic manifold that can be used to create new typefaces. The second piece of work generalises probabilistic PCA and Active Appearance Models to overcome a fundamental weakness; existing subspace models are unable to model image datasets that cannot be readily aligned. Instead, we learn a subspace model in a new context space, a deterministic function of an input “part map”, that implicitly encodes correspondence and thus brings the data into alignment. We illustrate the value of this approach by considering two example tasks: structured in-painting and appearance transfer.

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