  Predicting future frames for robotic surgical video is an interesting,
important yet extremely challenging problem, given that the operative tasks may
have complex dynamics. Existing approaches on future prediction of natural
videos were based on either deterministic models or stochastic models,
including deep recurrent neural networks, optical flow, and latent space
modeling. However, the potential in predicting meaningful movements of robots
with dual arms in surgical scenarios has not been tapped so far, which is
typically more challenging than forecasting independent motions of one arm
robots in natural scenarios. In this paper, we propose a ternary prior guided
variational autoencoder (TPG-VAE) model for future frame prediction in robotic
surgical video sequences. Besides content distribution, our model learns motion
distribution, which is novel to handle the small movements of surgical tools.
Furthermore, we add the invariant prior information from the gesture class into
the generation process to constrain the latent space of our model. To our best
knowledge, this is the first time that the future frames of dual arm robots are
predicted considering their unique characteristics relative to general robotic
videos. Experiments demonstrate that our model gains more stable and realistic
future frame prediction scenes with the suturing task on the public JIGSAWS
dataset.
