JointHOI: Jointly Generating Contact Maps Enhances Hand Object Interaction Generation

2026-07-02 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

JointHOI is a novel single-stage diffusion framework designed to enhance text-driven hand-object interaction (HOI) generation, a critical area for immersive applications and robotics. This framework jointly generates 3D hand-object motion and dynamic, distance-based contact maps directly from text input. Unlike prior multi-stage pipelines that struggle with temporally evolving contact, JointHOI treats contact as an auxiliary inner modality, allowing the model to learn contact motion coupling during training. During inference, its contact-guided sampling mechanism enforces consistency between the generated contact maps and the motion-implied geometry, which significantly improves temporal stability and effectively reduces common artifacts like floating and interpenetration. Experimental results on the GRAB and ARCTIC datasets consistently demonstrate that JointHOI achieves superior text adherence and physical plausibility compared to existing methods.

Key takeaway

For Computer Vision Engineers developing text-driven hand-object interaction systems, JointHOI offers a significant advancement. You should consider adopting this single-stage diffusion framework to overcome challenges with temporal stability and physical plausibility. Its joint generation of motion and dynamic contact maps directly from text will help you reduce common artifacts like floating and interpenetration, leading to more realistic and robust interactive applications. Evaluate its performance against your current methods using datasets like GRAB or ARCTIC.

Key insights

JointHOI's single-stage diffusion jointly generates 3D hand-object motion and dynamic contact maps, improving physical plausibility.

Principles

• Joint generation of motion and contact maps enhances physical realism.
• Treating contact as an auxiliary modality improves temporal stability.
• Contact-guided sampling reduces interpenetration and floating artifacts.

Method

JointHOI is a single-stage diffusion framework. It jointly generates 3D hand-object motion and dynamic contact maps from text, learning contact-motion coupling. Inference uses contact-guided sampling for consistency.

In practice

• Apply to text-driven HOI for robotics.
• Use for immersive application development.
• Evaluate on GRAB and ARCTIC datasets.

Topics

• Hand-Object Interaction
• Diffusion Models
• 3D Motion Generation
• Contact Maps
• Robotics
• Immersive Applications

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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• Improving and Evaluating Hand-Object Interaction Detection — HOI-DETR significantly advances hand-object interaction detection through a novel architecture and comprehensive evaluation.
• UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception — UMI-3D enhances robot manipulation data collection via LiDAR-centric SLAM and multimodal sensor fusion.
• MOCHI: Motion Enhancement of Collaborative Human-object Interactions — MOCHI enhances complex multi-human object interaction data by optimizing grasps and refining full-body motion with interaction priors.
• CHOIR: Contact-aware 4D Hand-Object Interaction Reconstruction — CHOIR reconstructs 4D hand-object interactions from monocular video, explicitly using contact as a coupling signal for improved consistency.
• Hand Trajectory Fusion for Egocentric Natural Language Query Grounding — Integrating hand trajectory data significantly improves egocentric video NLQ grounding for interaction-focused queries.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.