Example-Based Image Synthesis of Articulated Figures

We present a method for learning complex appearance mappings. such as occur with images of articulated objects. Traditional interpolation networks fail on this case since appearance is not necessarily a smooth function nor a linear manifold for articulated objects. We define an ap(cid: 173) pearance mapping from examples by constructing a set of independently smooth interpolation networks; these networks can cover overlapping re(cid: 173) gions of parameter space. A set growing procedure is used to find ex(cid: 173) ample clusters which are well-approximated within their convex hull; interpolation then proceeds only within these sets of examples. With this method physically valid images are produced even in regions of param(cid: 173) eter space where nearby examples have different appearances. We show results generating both simulated and real arm images.

Authors

• Trevor Darrell

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