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

• URL: http://arxiv.org/abs/2505.12774v1
• Date: Mon, 19 May 2025 07:02:12 GMT
• Title: UniHM: Universal Human Motion Generation with Object Interactions in Indoor Scenes
• Authors: Zichen Geng, Zeeshan Hayder, Wei Liu, Ajmal Mian,
• Abstract summary: We propose UniHM, a unified motion language model that leverages diffusion-based generation for scene-aware human motion.<n>UniHM is the first framework to support both Text-to-Motion and Text-to-Human-Object Interaction (HOI) in complex 3D scenes.<n>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 that surpasses traditional VQ-VAEs in both reconstruction accuracy and generative performance; and (3) an enriched version of
• Score: 26.71077287710599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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