This work addresses the problem of changing grasp configurations on objects with an unknown shape through in-hand manipulation. Our approach leverages shape priors, learned as deep generative models, to infer novel object shapes from partial visual sensing. The Dexterous Manipulation Graph method is extended to build incrementally and account for object shape uncertainty when planning a sequence of manipulation actions. We show that our approach successfully solves in-hand manipulation tasks with unknown objects, and demonstrate the validity of these solutions with robot experiments.