UniHM: Universal Human Motion Generation with Object Interactions in Indoor Scenes

TL;DR

UniHM is a novel diffusion-based model that enables realistic, scene-aware human motion synthesis from text prompts, supporting complex object interactions in indoor environments.

Contribution

It introduces a unified framework supporting both Text-to-Motion and Text-to-Human-Object Interaction generation, with innovative motion representation and a new dataset enhancement.

Findings

Achieves competitive results on OMOMO benchmark for HOI synthesis.

Yields strong performance on HumanML3D for general motion generation.

Outperforms traditional VQ-VAEs in motion reconstruction and generation.

Abstract

Human motion synthesis in complex scenes presents a fundamental challenge, extending beyond conventional Text-to-Motion tasks by requiring the integration of diverse modalities such as static environments, movable objects, natural language prompts, and spatial waypoints. Existing language-conditioned motion models often struggle with scene-aware motion generation due to limitations in motion tokenization, which leads to information loss and fails to capture the continuous, context-dependent nature of 3D human movement. To address these issues, we propose UniHM, a unified motion language model that leverages diffusion-based generation for synthesizing scene-aware human motion. UniHM is the first framework to support both Text-to-Motion and Text-to-Human-Object Interaction (HOI) in complex 3D scenes. Our approach introduces three key contributions: (1) a mixed-motion representation that fuses continuous 6DoF motion with discrete local motion tokens to improve motion realism; (2) a novel Look-Up-Free Quantization VAE (LFQ-VAE) that surpasses traditional VQ-VAEs in both reconstruction accuracy and generative performance; and (3) an enriched version of the Lingo dataset augmented with HumanML3D annotations, providing stronger supervision for scene-specific motion learning. Experimental results demonstrate that UniHM achieves comparative performance on the OMOMO benchmark for text-to-HOI synthesis and yields competitive results on HumanML3D for general text-conditioned motion generation.