Peizhuo Li (ETH Zurich) - Generative Motion Matching
The generation of natural, varied, and detailed motions is a core problem in computer animation. We present a generative model – generative motion matching – that “mines” as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, our method inherits the training-free nature and the superior quality of the well-known Motion Matching method. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly.
Host: Rana Hanocka
Peizhuo Li is a PhD student at Interactive Geometry Lab under the supervision of Prof. Olga Sorkine-Hornung, at ETH Zurich. His research interest lies in the intersection between deep learning and computer graphics. In particular, he is interested in practical problems related to character animation. Prior to his PhD study, he was a bachelor student at Peking University and worked as research assistant at Visual Computing and Learning lab, advised by Prof. Baoquan Chen.