  Prediction of human actions in social interactions has important applications
in the design of social robots or artificial avatars. In this paper, we model
human interaction generation as a discrete multi-sequence generation problem
and present SocialInteractionGAN, a novel adversarial architecture for
conditional interaction generation. Our model builds on a recurrent
encoder-decoder generator network and a dual-stream discriminator. This
architecture allows the discriminator to jointly assess the realism of
interactions and that of individual action sequences. Within each stream a
recurrent network operating on short subsequences endows the output signal with
local assessments, better guiding the forthcoming generation. Crucially,
contextual information on interacting participants is shared among agents and
reinjected in both the generation and the discriminator evaluation processes.
We show that the proposed SocialInteractionGAN succeeds in producing high
realism action sequences of interacting people, comparing favorably to a
diversity of recurrent and convolutional discriminator baselines. Evaluations
are conducted using modified Inception Score and Fr{\'e}chet Inception Distance
metrics, that we specifically design for discrete sequential generated data.
The distribution of generated sequences is shown to approach closely that of
real data. In particular our model properly learns the dynamics of interaction
sequences, while exploiting the full range of actions.
