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arXiv · 2607.07880

GIRAF: Towards Generalizable Human Interactions with Articulated Objects

Xiaohan Zhang·Sebastian Starke·Alexander Winkler·Federica Bogo·Samir Aroudj·Yuting Ye
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Abstract

Synthesizing realistic full-body human interactions with articulated objects is a fundamental challenge for embodied AI and graphics, with applications in robotics training and virtual agents. Existing models remain limited: some focus on simple activities with static objects, while others restrict attention to hand-only manipulation. This leaves open the problem of generating coordinated full-body motion that approaches, manipulates, and moves articulated objects in a realistic and generalizable way. The key difficulty lies in reasoning jointly about locomotion, fine-grained contact, and object articulation. Models must capture subtle hand-object correspondences that transfer across object geometries, while also producing seamless transitions from navigation to manipulation. At the same time, the scarcity of large-scale paired motion-scene data makes it difficult to generalize across diverse object positions and shapes. We introduce a text-conditioned diffusion model that addresses these challenges through three core ideas: an object-centric representation that unifies hand-object contact with object surfaces, a mixed-domain training strategy that balances locomotion and interaction, and a contact-based augmentation scheme that expands training diversity. Through experiments, our method demonstrated strong generalization to unseen object configurations, surpassing current state-of-the-art methods.

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Xiaohan Zhang, Sebastian Starke, Alexander Winkler, Federica Bogo, Samir Aroudj, Yuting Ye. 2026-07-08. GIRAF: Towards Generalizable Human Interactions with Articulated Objects. https://arxiv.org/abs/2607.07880

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