  Human motion prediction, which plays a key role in computer vision, generally
requires a past motion sequence as input. However, in real applications, a
complete and correct past motion sequence can be too expensive to achieve. In
this paper, we propose a novel approach to predicting future human motions from
a much weaker condition, i.e., a single image, with mixture density networks
(MDN) modeling. Contrary to most existing deep human motion prediction
approaches, the multimodal nature of MDN enables the generation of diverse
future motion hypotheses, which well compensates for the strong stochastic
ambiguity aggregated by the single input and human motion uncertainty. In
designing the loss function, we further introduce the energy-based formulation
to flexibly impose prior losses over the learnable parameters of MDN to
maintain motion coherence as well as improve the prediction accuracy by
customizing the energy functions. Our trained model directly takes an image as
input and generates multiple plausible motions that satisfy the given
condition. Extensive experiments on two standard benchmark datasets demonstrate
the effectiveness of our method in terms of prediction diversity and accuracy.
