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The hierarchical\nstructure of CNN makes it capable of capturing both spatial and temporal\ncorrelations effectively. In our proposed approach,a convolutional long-term\nencoder is used to encode the whole given motion sequence into a long-term\nhidden variable, which is used with a decoder to predict the remainder of the\nsequence. The decoder itself also has an encoder-decoder structure, in which\nthe short-term encoder encodes a shorter sequence to a short-term hidden\nvariable, and the spatial decoder maps the long and short-term hidden variable\nto motion predictions. By using such a model, we are able to capture both\ninvariant and dynamic information of human motion, which results in more\naccurate predictions. Experiments show that our algorithm outperforms the\nstate-of-the-art methods on the Human3.6M and CMU Motion Capture datasets. 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