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Generative Adversarial Networks (GANs) have shown remarkable successes ingenerating realistic images and interpolating changes between images. Existingmodels, however, do not take into account physical contexts behind images ingenerating the images, which may cause unrealistic changes. Furthermore, it isdifficult to generate the changes at a specific timing and they often do notmatch with actual changes. This paper proposes a novel GAN, named PhysicalContext and Timing aware sequence generating GANs (PCTGAN), that generates animage in a sequence at a specific timing between two images with consideringphysical contexts behind them. Our method consists of three components: anencoder, a generator, and a discriminator. The encoder estimates latent vectorsfrom the beginning and ending images, their timings, and a target timing. Thegenerator generates images and the physical contexts at the beginning, ending,and target timing from the corresponding latent vectors. The discriminatordiscriminates whether the generated images and contexts are real or not. In theexperiments, PCTGAN is applied to a data set of sequential changes of shapes indie forging processes. We show that both timing and physical contexts areeffective in generating sequential images.

Physical Context and Timing Aware Sequence Generating GANs