Generative Models for Shape and Appearance

July 23, 2015
Neill Campbell | University of Bath

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.

Speaker Details

Neill recently joined the Department of Computer Science at the University of Bath as a Lecturer, moving from a Post Doctoral position at University College London (working with Jan Kautz and Simon Prince) having previously obtained my MEng and PhD from the University of Cambridge under the supervision of Roberto Cipolla. His research involves the application of Machine Learning techniques to Computer Vision and Graphics, with particular interest in modelling 2D and 3D shape and appearance.