  For a given scene, humans can easily reason for the locations and pose to
place objects. Designing a computational model to reason about these
affordances poses a significant challenge, mirroring the intuitive reasoning
abilities of humans. This work tackles the problem of realistic human insertion
in a given background scene termed as \textbf{Semantic Human Placement}. This
task is extremely challenging given the diverse backgrounds, scale, and pose of
the generated person and, finally, the identity preservation of the person. We
divide the problem into the following two stages \textbf{i)} learning
\textit{semantic masks} using text guidance for localizing regions in the image
to place humans and \textbf{ii)} subject-conditioned inpainting to place a
given subject adhering to the scene affordance within the \textit{semantic
masks}. For learning semantic masks, we leverage rich object-scene priors
learned from the text-to-image generative models and optimize a novel
parameterization of the semantic mask, eliminating the need for large-scale
training. To the best of our knowledge, we are the first ones to provide an
effective solution for realistic human placements in diverse real-world scenes.
The proposed method can generate highly realistic scene compositions while
preserving the background and subject identity. Further, we present results for
several downstream tasks - scene hallucination from a single or multiple
generated persons and text-based attribute editing. With extensive comparisons
against strong baselines, we show the superiority of our method in realistic
human placement.
