  Human motion prediction model has applications in various fields of computer
vision. Without taking into account the inherent stochasticity in the
prediction of future pose dynamics, such methods often converges to a
deterministic undesired mean of multiple probable outcomes. Devoid of this, we
propose a novel probabilistic generative approach called Bidirectional Human
motion prediction GAN, or BiHMP-GAN. To be able to generate multiple probable
human-pose sequences, conditioned on a given starting sequence, we introduce a
random extrinsic factor r, drawn from a predefined prior distribution.
Furthermore, to enforce a direct content loss on the predicted motion sequence
and also to avoid mode-collapse, a novel bidirectional framework is
incorporated by modifying the usual discriminator architecture. The
discriminator is trained also to regress this extrinsic factor r, which is used
alongside with the intrinsic factor (encoded starting pose sequence) to
generate a particular pose sequence. To further regularize the training, we
introduce a novel recursive prediction strategy. In spite of being in a
probabilistic framework, the enhanced discriminator architecture allows
predictions of an intermediate part of pose sequence to be used as a
conditioning for prediction of the latter part of the sequence. The
bidirectional setup also provides a new direction to evaluate the prediction
quality against a given test sequence. For a fair assessment of BiHMP-GAN, we
report performance of the generated motion sequence using (i) a critic model
trained to discriminate between real and fake motion sequence, and (ii) an
action classifier trained on real human motion dynamics. Outcomes of both
qualitative and quantitative evaluations, on the probabilistic generations of
the model, demonstrate the superiority of BiHMP-GAN over previously available
methods.
