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Modeling Human Motion Using Binary Latent Variables

Modeling Human Motion Using Binary Latent Variables,Graham W. Taylor,Geoffrey E. Hinton,Sam T. Roweis

Modeling Human Motion Using Binary Latent Variables   (Citations: 57)
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We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued "visible" variables that represent joint angles. The latent and visible variabl es at each time step re- ceive directed connections from the visible variables at th e last few time-steps. Such an architecture makes on-line inference efficient and a llows us to use a sim- ple approximate learning procedure. After training, the model finds a single set of parameters that simultaneously capture several differe nt kinds of motion. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motio n capture. Website: http://www.cs.toronto.edu/∼gwtaylor/publications/nips2006mhmublv/
Conference: Neural Information Processing Systems - NIPS , pp. 1345-1352, 2006
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