MixSyn: Learning Composition and Style for Multi-Source Image Synthesis
This addresses the issue of unauthorized impersonation in generative models, offering a novel approach for multi-source image synthesis with potential applications in creative and security domains.
The paper tackles the problem of generating synthetic images that impersonate real individuals without consent by introducing MixSyn, a method that learns novel compositions from multiple sources to create images as a mix of regions, achieving high-quality reconstructions and outperforming state-of-the-art approaches in quality, diversity, realism, and expressive power.
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.