Learning Human Motion Models for Long-Term Predictions
Published: Oct 1, 2017
Abstract
We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM (DAELSTM), is capable of synthesizing natural looking motion sequences over long-time horizons1 without catastrophic drift or motion degradation. The model consists of two components, a 3-layer recurrent neural network to model temporal aspects and a novel autoencoder that is trained to...
Paper Details
Title
Learning Human Motion Models for Long-Term Predictions
Published Date
Oct 1, 2017
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