  Motion behaviour is driven by several factors -- goals, presence and actions
of neighbouring agents, social relations, physical and social norms, the
environment with its variable characteristics, and further. Most factors are
not directly observable and must be modelled from context. Trajectory
prediction, is thus a hard problem, and has seen increasing attention from
researchers in the recent years. Prediction of motion, in application, must be
realistic, diverse and controllable. In spite of increasing focus on multimodal
trajectory generation, most methods still lack means for explicitly controlling
different modes of the data generation. Further, most endeavours invest heavily
in designing special mechanisms to learn the interactions in latent space. We
present Conditional Speed GAN (CSG), that allows controlled generation of
diverse and socially acceptable trajectories, based on user controlled speed.
During prediction, CSG forecasts future speed from latent space and conditions
its generation based on it. CSG is comparable to state-of-the-art GAN methods
in terms of the benchmark distance metrics, while being simple and useful for
simulation and data augmentation for different contexts such as fast or slow
paced environments. Additionally, we compare the effect of different
aggregation mechanisms and show that a naive approach of concatenation works
comparable to its attention and pooling alternatives.
