  Recent work on image anonymization has shown that generative adversarial
networks (GANs) can generate near-photorealistic faces to anonymize
individuals. However, scaling up these networks to the entire human body has
remained a challenging and yet unsolved task. We propose a new anonymization
method that generates realistic humans for in-the-wild images. A key part of
our design is to guide adversarial nets by dense pixel-to-surface
correspondences between an image and a canonical 3D surface. We introduce
Variational Surface-Adaptive Modulation (V-SAM) that embeds surface information
throughout the generator. Combining this with our novel discriminator surface
supervision loss, the generator can synthesize high quality humans with diverse
appearances in complex and varying scenes. We demonstrate that surface guidance
significantly improves image quality and diversity of samples, yielding a
highly practical generator. Finally, we show that our method preserves data
usability without infringing privacy when collecting image datasets for
training computer vision models. Source code and appendix is available at:
\href{https://github.com/hukkelas/full_body_anonymization}{github.com/hukkelas/full\_body\_anonymization}
