We present a novel deep learning architecture for probabilistic future prediction from video. We predict the future semantics, geometry and motion of complex real-world urban scenes and use this representation to control an autonomous vehicle. This work is the first to jointly
predict ego-motion, static scene, and the motion of dynamic agents in a
probabilistic manner, which allows sampling consistent, highly probable
futures from a compact latent space. Our model learns a representation from RGB video with a spatio-temporal convolutional module. The
learned representation can be explicitly decoded to future semantic segmentation, depth, and optical flow, in addition to being an input to a
learnt driving policy. To model the stochasticity of the future, we introduce a conditional variational approach which minimises the divergence
between the present distribution (what could happen given what we have
seen) and the future distribution (what we observe actually happens).
During inference, diverse futures are generated by sampling from the
present distribution.Toshiba Europe, grant G10045