  Synthetic images created by generative models increase in quality and
expressiveness as newer models utilize larger datasets and novel architectures.
Although this photorealism is a positive side-effect from a creative
standpoint, it becomes problematic when such generative models are used for
impersonation without consent. Most of these approaches are built on the
partial transfer between source and target pairs, or they generate completely
new samples based on an ideal distribution, still resembling the closest real
sample in the dataset. We propose MixSyn (read as " mixin' ") for learning
novel fuzzy compositions from multiple sources and creating novel images as a
mix of image regions corresponding to the compositions. MixSyn not only
combines uncorrelated regions from multiple source masks into a coherent
semantic composition, but also generates mask-aware high quality
reconstructions of non-existing images. We compare MixSyn to state-of-the-art
single-source sequential generation and collage generation approaches in terms
of quality, diversity, realism, and expressive power; while also showcasing
interactive synthesis, mix & match, and edit propagation tasks, with no mask
dependency.
