  We present a method for generating alpha mattes using a limited data source.
We pretrain a novel transformerbased model (StyleMatte) on portrait datasets.
We utilize this model to provide image-mask pairs for the StyleGAN3-based
network (StyleMatteGAN). This network is trained unsupervisedly and generates
previously unseen imagemask training pairs that are fed back to StyleMatte. We
demonstrate that the performance of the matte pulling network improves during
this cycle and obtains top results on the human portraits and state-of-the-art
metrics on animals dataset. Furthermore, StyleMatteGAN provides
high-resolution, privacy-preserving portraits with alpha mattes, making it
suitable for various image composition tasks. Our code is available at
https://github.com/chroneus/stylematte
