  Generative Adversarial Networks (GANs) have shown remarkable successes in
generating realistic images and interpolating changes between images. Existing
models, however, do not take into account physical contexts behind images in
generating the images, which may cause unrealistic changes. Furthermore, it is
difficult to generate the changes at a specific timing and they often do not
match with actual changes. This paper proposes a novel GAN, named Physical
Context and Timing aware sequence generating GANs (PCTGAN), that generates an
image in a sequence at a specific timing between two images with considering
physical contexts behind them. Our method consists of three components: an
encoder, a generator, and a discriminator. The encoder estimates latent vectors
from the beginning and ending images, their timings, and a target timing. The
generator generates images and the physical contexts at the beginning, ending,
and target timing from the corresponding latent vectors. The discriminator
discriminates whether the generated images and contexts are real or not. In the
experiments, PCTGAN is applied to a data set of sequential changes of shapes in
die forging processes. We show that both timing and physical contexts are
effective in generating sequential images.
