  What does human pose tell us about a scene? We propose a task to answer this
question: given human pose as input, hallucinate a compatible scene. Subtle
cues captured by human pose -- action semantics, environment affordances,
object interactions -- provide surprising insight into which scenes are
compatible. We present a large-scale generative adversarial network for
pose-conditioned scene generation. We significantly scale the size and
complexity of training data, curating a massive meta-dataset containing over 19
million frames of humans in everyday environments. We double the capacity of
our model with respect to StyleGAN2 to handle such complex data, and design a
pose conditioning mechanism that drives our model to learn the nuanced
relationship between pose and scene. We leverage our trained model for various
applications: hallucinating pose-compatible scene(s) with or without humans,
visualizing incompatible scenes and poses, placing a person from one generated
image into another scene, and animating pose. Our model produces diverse
samples and outperforms pose-conditioned StyleGAN2 and Pix2Pix/Pix2PixHD
baselines in terms of accurate human placement (percent of correct keypoints)
and quality (Frechet inception distance).
