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Generative Models for Complex Shapes
-------------------------------------------------------------------- ***Joint Seminar*** -------------------------------------------------------------------- The Hong Kong University of Science & Technology Department of Computer Science and Engineering Center of Visual Computing and Image Science -------------------------------------------------------------------- Speaker: Dr. Vladlen Koltun Department of Computer Science Stanford University Title: "Generative Models for Complex Shapes" Date: Monday, 12 November 2012 Time: 4:00pm - 5:00pm Venue: Lecture Theatre F (near lifts 25/26), HKUST Abstract: I will discuss computational modeling of complex object categories. Can we characterize the space of all chairs, or airplanes, or single-family homes? Can such a characterization be generative, allowing the synthesis of valid new chairs, or airplanes, or single-family homes? I will present recent work that answers these questions in the affirmative. Our key idea is to treat shapes from complex domains as assemblies of components, and to learn relationships between components that characterize plausible shapes from particular domains. For example, an airplane may be composed from a fuselage, wings, stabilizers, engines, and other components. The presence and shape of all these parts are interrelated by complex probabilistic dependencies: the presence of a jet engine implies a decreased likelihood of propellers, and well as a decreased likelihood of the plane being a biplane. It also increases the likelihood of a smooth and aerodynamic fuselage shape. I will present representations that model these networks of relationships, and provide a general approach to generative modeling of the structure and semantics of three-dimensional shapes from complex domains. These representations lead to new kinds of easy-to-use 3D modeling tools. ******************** Biography: Vladlen Koltun is a faculty member in the Computer Science Department at Stanford University, working in computer graphics, computer vision, and machine learning. His prior work in theoretical computer science was recognized with the NSF CAREER Award, the Alfred P. Sloan Fellowship, and the Machtey Award.