Disentangled Dynamic Representations from Unordered Data
We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input.
December 2, 2018
Symposium on Advances in Approximate Bayesian Inference 2018
Authors
Leonhard Helminger (Disney Research/ETH Joint PhD)
Aziz Djelouah (Disney Research)
Markus Gross (Disney Research/ETH Zurich)
Romann Weber (Disney Research)
Disentangled Dynamic Representations from Unordered Data
Our approach exploits regularities in sequential data that exist regardless of the order in which the data is viewed. The result of our factorized graphical model is a well-organized and coherent latent space for data dynamics. We demonstrate our method on several synthetic dynamic datasets and real video data featuring various facial expressions and head poses.
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