ViHOI: Human-Object Interaction Synthesis with Visual Priors

• URL: http://arxiv.org/abs/2603.24383v1
• Date: Wed, 25 Mar 2026 15:02:06 GMT
• Title: ViHOI: Human-Object Interaction Synthesis with Visual Priors
• Abstract summary: ViHOI is a novel framework that enables diffusion-based generative models to leverage rich, task-specific priors from 2D images.<n>Our framework is trained on motion-rendered images from the dataset to ensure strict semantic alignment between visual inputs and motion sequences.
• Score: 21.133510317025635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating realistic and physically plausible 3D Human-Object Interactions (HOI) remains a key challenge in motion generation. One primary reason is that describing these physical constraints with words alone is difficult. To address this limitation, we propose a new paradigm: extracting rich interaction priors from easily accessible 2D images. Specifically, we introduce ViHOI, a novel framework that enables diffusion-based generative models to leverage rich, task-specific priors from 2D images to enhance generation quality. We utilize a large Vision-Language Model (VLM) as a powerful prior-extraction engine and adopt a layer-decoupled strategy to obtain visual and textual priors. Concurrently, we design a Q-Former-based adapter that compresses the VLM's high-dimensional features into compact prior tokens, which significantly facilitates the conditional training of our diffusion model. Our framework is trained on motion-rendered images from the dataset to ensure strict semantic alignment between visual inputs and motion sequences. During inference, it leverages reference images synthesized by a text-to-image generation model to improve generalization to unseen objects and interaction categories. Experimental results demonstrate that ViHOI achieves state-of-the-art performance, outperforming existing methods across multiple benchmarks and demonstrating superior generalization.

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