  Can we synthesize 3D humans interacting with scenes without learning from any
3D human-scene interaction data? We propose GenZI, the first zero-shot approach
to generating 3D human-scene interactions. Key to GenZI is our distillation of
interaction priors from large vision-language models (VLMs), which have learned
a rich semantic space of 2D human-scene compositions. Given a natural language
description and a coarse point location of the desired interaction in a 3D
scene, we first leverage VLMs to imagine plausible 2D human interactions
inpainted into multiple rendered views of the scene. We then formulate a robust
iterative optimization to synthesize the pose and shape of a 3D human model in
the scene, guided by consistency with the 2D interaction hypotheses. In
contrast to existing learning-based approaches, GenZI circumvents the
conventional need for captured 3D interaction data, and allows for flexible
control of the 3D interaction synthesis with easy-to-use text prompts.
Extensive experiments show that our zero-shot approach has high flexibility and
generality, making it applicable to diverse scene types, including both indoor
and outdoor environments.
