InteractionGAN: ImageLevel Interaction using Generative Adversarial Networks We present a novel strategy for imagelevel interaction that is applicable to a single image without any prior structural knowledge, such as object status or reconstructed 3D models. By training sets of input image and interaction pairs using a target image, our model can generate result images by applying the desired interaction to new unseen images. The proposed method is differentiated from previous approaches for changing poses, which requires absolute statuses for training images or estimated 3D model with reconstruction errors. Based on the conceptual analysis of encoderdecoder networks, we propose a novel generator network architecture containing a feature converter network, which is suitable for applying interactions to images. We also implement a discriminator network for training, which is a wellknown technique for generative adversarial networks. Experimental results demonstrate that the proposed method successfully generates result images with applied interactions without any prior knowledge. We expect that our method will provide insights into novel interaction schemes for augmented reality by reflecting interactions onto real scenes and providing more realistic user experiences.

A StyleBased Generator Architecture for Generative Adversarial Networks We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of highlevel attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scalespecific control of the synthesis. The new generator improves the stateoftheart in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and highquality dataset of human faces.