  A large body of recent work targets semantically conditioned image
generation. Most such methods focus on the narrower task of pose transfer and
ignore the more challenging task of subject transfer that consists in not only
transferring the pose but also the appearance and background. In this work, we
introduce SCAM (Semantic Cross Attention Modulation), a system that encodes
rich and diverse information in each semantic region of the image (including
foreground and background), thus achieving precise generation with emphasis on
fine details. This is enabled by the Semantic Attention Transformer Encoder
that extracts multiple latent vectors for each semantic region, and the
corresponding generator that exploits these multiple latents by using semantic
cross attention modulation. It is trained only using a reconstruction setup,
while subject transfer is performed at test time. Our analysis shows that our
proposed architecture is successful at encoding the diversity of appearance in
each semantic region. Extensive experiments on the iDesigner and CelebAMask-HD
datasets show that SCAM outperforms SEAN and SPADE; moreover, it sets the new
state of the art on subject transfer.
