  We present a model that uses a single first-person image to generate an
egocentric basketball motion sequence in the form of a 12D camera configuration
trajectory, which encodes a player's 3D location and 3D head orientation
throughout the sequence. To do this, we first introduce a future convolutional
neural network (CNN) that predicts an initial sequence of 12D camera
configurations, aiming to capture how real players move during a one-on-one
basketball game. We also introduce a goal verifier network, which is trained to
verify that a given camera configuration is consistent with the final goals of
real one-on-one basketball players. Next, we propose an inverse synthesis
procedure to synthesize a refined sequence of 12D camera configurations that
(1) sufficiently matches the initial configurations predicted by the future
CNN, while (2) maximizing the output of the goal verifier network. Finally, by
following the trajectory resulting from the refined camera configuration
sequence, we obtain the complete 12D motion sequence.
  Our model generates realistic basketball motion sequences that capture the
goals of real players, outperforming standard deep learning approaches such as
recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and
generative adversarial networks (GANs).
