  Prediction of human actions in social interactions has important applications
in the design of social robots or artificial avatars. In this paper, we focus
on a unimodal representation of interactions and propose to tackle interaction
generation in a data-driven fashion. In particular, 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, that jointly evaluates the
realism of interactions and individual action sequences and operates at
different time scales. Crucially, contextual information on interacting
participants is shared among agents and reinjected in both the generation and
the discriminator evaluation processes. Experiments show that albeit dealing
with low dimensional data, SocialInteractionGAN succeeds in producing high
realism action sequences of interacting people, comparing favorably to a
diversity of recurrent and convolutional discriminator baselines, and we argue
that this work will constitute a first stone towards higher dimensional and
multimodal interaction generation. Evaluations are conducted using classical
GAN metrics, that we specifically adapt for discrete sequential data. Our model
is shown to properly learn the dynamics of interaction sequences, while
exploiting the full range of available actions.
