  This paper presents a novel method for generating diverse 3D human poses in
scenes with semantic control. Existing methods heavily rely on the human-scene
interaction dataset, resulting in a limited diversity of the generated human
poses. To overcome this challenge, we propose to decouple the pose and
interaction generation process. Our approach consists of three stages: pose
generation, contact generation, and putting human into the scene. We train a
pose generator on the human dataset to learn rich pose prior, and a contact
generator on the human-scene interaction dataset to learn human-scene contact
prior. Finally, the placing module puts the human body into the scene in a
suitable and natural manner. The experimental results on the PROX dataset
demonstrate that our method produces more physically plausible interactions and
exhibits more diverse human poses. Furthermore, experiments on the MP3D-R
dataset further validates the generalization ability of our method.
