{"ID":6267750,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-11T19:24:34.872436639Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07880","arxiv_id":"2607.07880","title":"GIRAF: Towards Generalizable Human Interactions with Articulated Objects","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.","short_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-on...","url_abs":"https://arxiv.org/abs/2607.07880","url_pdf":"https://arxiv.org/pdf/2607.07880v1","authors":"[\"Xiaohan Zhang\",\"Sebastian Starke\",\"Alexander Winkler\",\"Federica Bogo\",\"Samir Aroudj\",\"Yuting Ye\"]","published":"2026-07-08T19:30:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}