GIRAF: Towards Generalizable Human Interactions with Articulated Objects
GIRAF is a text-conditioned diffusion model for generating realistic full-body interactions with articulated objects. It addresses limitations of prior works by jointly reasoning about locomotion, contact, and articulation, using an object-centric representation, mixed-domain training, and contact-based augmentation, achieving strong generalization to unseen object configurations.
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[Submitted on 8 Jul 2026]
Title:GIRAF: Towards Generalizable Human Interactions with Articulated Objects
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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.
Comments: 12 pages, 6 figures, 3 tables. Accepted at the Third Workshop on Human Motion Generation (HuMoGen), CVPR 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
ACM classes: I.3.7; I.2.10
Cite as: arXiv:2607.07880 [cs.CV]
(or arXiv:2607.07880v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.07880
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xiaohan Zhang [view email] [v1] Wed, 8 Jul 2026 19:30:44 UTC (6,268 KB)
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