Multimodal Priors-Augmented Text-Driven 3D Human-Object Interaction Generation

• URL: http://arxiv.org/abs/2602.10659v1
• Date: Wed, 11 Feb 2026 09:04:28 GMT
• Title: Multimodal Priors-Augmented Text-Driven 3D Human-Object Interaction Generation
• Authors: Yin Wang, Ziyao Zhang, Zhiying Leng, Haitian Liu, Frederick W. B. Li, Mu Li, Xiaohui Liang,
• Abstract summary: We address the challenging task of text-driven 3D human-object interaction (HOI) motion generation.<n>Existing methods primarily rely on a direct text-to-HOI mapping.<n>We propose MP-HOI, a novel framework grounded in four core insights.
• Score: 26.16137102387553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the challenging task of text-driven 3D human-object interaction (HOI) motion generation. Existing methods primarily rely on a direct text-to-HOI mapping, which suffers from three key limitations due to the significant cross-modality gap: (Q1) sub-optimal human motion, (Q2) unnatural object motion, and (Q3) weak interaction between humans and objects. To address these challenges, we propose MP-HOI, a novel framework grounded in four core insights: (1) Multimodal Data Priors: We leverage multimodal data (text, image, pose/object) from large multimodal models as priors to guide HOI generation, which tackles Q1 and Q2 in data modeling. (2) Enhanced Object Representation: We improve existing object representations by incorporating geometric keypoints, contact features, and dynamic properties, enabling expressive object representations, which tackles Q2 in data representation. (3) Multimodal-Aware Mixture-of-Experts (MoE) Model: We propose a modality-aware MoE model for effective multimodal feature fusion paradigm, which tackles Q1 and Q2 in feature fusion. (4) Cascaded Diffusion with Interaction Supervision: We design a cascaded diffusion framework that progressively refines human-object interaction features under dedicated supervision, which tackles Q3 in interaction refinement. Comprehensive experiments demonstrate that MP-HOI outperforms existing approaches in generating high-fidelity and fine-grained HOI motions.

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