Diverse 3D Human Pose Generation in Scenes based on Decoupled Structure

TL;DR

This paper introduces a decoupled approach for generating diverse and physically plausible 3D human poses in scenes, improving diversity and interaction realism by separating pose and contact generation.

Contribution

The novel decoupling of pose and contact generation processes enhances diversity and realism in 3D human pose synthesis within scenes.

Findings

Produces more physically plausible interactions

Generates more diverse human poses

Demonstrates good generalization on multiple datasets

Abstract

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.