Generating Novel Scene Compositions from Single Images and Videos

Given a large dataset for training, GANs can achieve remarkable performance for the image synthesis task. However, training GANs in extremely low data regimes remains a challenge, as overfitting often occurs, leading to memorization or training divergence. In this work, we introduce SIV-GAN, an unconditional generative model that can generate new scene compositions from a single training image or a single video clip. We propose a two-branch discriminator architecture, with content and layout branches designed to judge internal content and scene layout realism separately from each other. This discriminator design enables synthesis of visually plausible, novel compositions of a scene, with varying content and layout, while preserving the context of the original sample. Compared to previous single-image GANs, our model generates more diverse, higher quality images, while not being restricted to a single image setting. We show that SIV-GAN successfully deals with a new challenging task of learning from a single video, for which prior GAN models fail to achieve synthesis of both high quality and diversity.