Friday, 30 November 09:00 - 10:45 | Peridot 206
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Generative Statistical Models for Semantic Motion Analysis and SynthesisWe introduce a new generative statistical model for semantic motion analysis and synthesis. We have demonstrated its power by exploring a wide variety of applications, ranging from automatic motion segmentation, recognition, and annotation, online/offline motion synthesis in both kinematics and behavior level, performance-based animation to semantic motion editing. Jianyuan Min, Texas A&M University |
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Terrain Runner: Control, Parameterization, Composition, and Planning for Highly Dynamic MotionsWe present methods for the control, parameterization, composition, and planning for highly dynamic motions. More specifically, we learn the skills required by real-time physics-based avatars to perform parkour-style fast terrain crossing using a mix of running, jumping, speed-vaulting, and drop-rolling. Libin Liu, Tsinghua University |
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Falling and Landing Motion Control for Character AnimationLanding safely is one of the most fundamental skills in many high dynamic activities such as martial arts, acrobatics and free running. We develop a physics-based landing controller that allows the character to fall from a wide range of initial conditions without inducing a large amount of joint stress. Sehoon Ha, Georgia Institute of Technology |
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Synthesis of Concurrent Object Manipulation TasksWe introduce a physics-based technique to synthesize human activities involving concurrent full-body manipulation of multiple objects. Given an environment map and manipulation graphs, our algorithm generates a continuous animation of the character manipulating multiple objects and environment features concurrently at various locations in a constrained environment. Yunfei Bai, Georgia Institute of Technology |