Scene Synthesis from Human Motion

TL;DR

This paper introduces SUMMON, a framework that synthesizes realistic and diverse scenes from human motion data by predicting contact points and optimizing object placement, reducing the need for costly scene capture.

Contribution

The paper presents ContactFormer, a novel contact predictor, and a scene synthesis method that leverages human motion to generate plausible scene layouts with minimal supervision.

Findings

SUMMON produces feasible and diverse scene layouts.

ContactFormer accurately predicts human-scene contact points.

The method can generate extensive human-scene interaction data.

Abstract

Large-scale capture of human motion with diverse, complex scenes, while immensely useful, is often considered prohibitively costly. Meanwhile, human motion alone contains rich information about the scene they reside in and interact with. For example, a sitting human suggests the existence of a chair, and their leg position further implies the chair's pose. In this paper, we propose to synthesize diverse, semantically reasonable, and physically plausible scenes based on human motion. Our framework, Scene Synthesis from HUMan MotiON (SUMMON), includes two steps. It first uses ContactFormer, our newly introduced contact predictor, to obtain temporally consistent contact labels from human motion. Based on these predictions, SUMMON then chooses interacting objects and optimizes physical plausibility losses; it further populates the scene with objects that do not interact with humans. Experimental results demonstrate that SUMMON synthesizes feasible, plausible, and diverse scenes and has the potential to generate extensive human-scene interaction data for the community.