Human-Aware 3D Scene Generation with Spatially-constrained Diffusion Models

TL;DR

This paper introduces a diffusion model-based approach for generating realistic 3D scenes from human motion sequences, effectively handling spatial constraints and reducing object collisions, advancing the realism of virtual environments.

Contribution

The authors propose a novel diffusion model framework with spatial collision guidance for human-aware 3D scene generation, improving over prior auto-regressive methods.

Findings

More natural and plausible 3D scenes generated

Significant reduction in human-object collisions

Enhanced diversity and accuracy of scene generation

Abstract

Generating 3D scenes from human motion sequences supports numerous applications, including virtual reality and architectural design. However, previous auto-regression-based human-aware 3D scene generation methods have struggled to accurately capture the joint distribution of multiple objects and input humans, often resulting in overlapping object generation in the same space. To address this limitation, we explore the potential of diffusion models that simultaneously consider all input humans and the floor plan to generate plausible 3D scenes. Our approach not only satisfies all input human interactions but also adheres to spatial constraints with the floor plan. Furthermore, we introduce two spatial collision guidance mechanisms: human-object collision avoidance and object-room boundary constraints. These mechanisms help avoid generating scenes that conflict with human motions while respecting layout constraints. To enhance the diversity and accuracy of human-guided scene generation, we have developed an automated pipeline that improves the variety and plausibility of human-object interactions in the existing 3D FRONT HUMAN dataset. Extensive experiments on both synthetic and real-world datasets demonstrate that our framework can generate more natural and plausible 3D scenes with precise human-scene interactions, while significantly reducing human-object collisions compared to previous state-of-the-art methods. Our code and data will be made publicly available upon publication of this work.