  Human action-anticipation methods predict what is the future action by
observing only a few portion of an action in progress. This is critical for
applications where computers have to react to human actions as early as
possible such as autonomous driving, human-robotic interaction, assistive
robotics among others. In this paper, we present a method for human action
anticipation by predicting the most plausible future human motion. We represent
human motion using Dynamic Images and make use of tailored loss functions to
encourage a generative model to produce accurate future motion prediction. Our
method outperforms the currently best performing action-anticipation methods by
4% on JHMDB-21, 5.2% on UT-Interaction and 5.1% on UCF 101-24 benchmarks.
