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Efficient training for future video generation based on hierarchical disentangled representation of latent variables
In this paper, we propose a novel method for generating future prediction videos with less memory usage than the conventional methods.
Long-Term Human Video Generation of Multiple Futures Using Poses
First, from an input human video, we generate sequences of future human poses (i. e., the image coordinates of their body-joints) via adversarial learning.
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