GenHOI: Generalizing Text-driven 4D Human-Object Interaction Synthesis for Unseen Objects
Shujia Li, Haiyu Zhang, Xinyuan Chen, Yaohui Wang, Yutong Ban

TL;DR
GenHOI introduces a two-stage framework combining object reconstruction and contact-aware diffusion to synthesize high-fidelity 4D human-object interactions, generalizing to unseen objects with state-of-the-art results.
Contribution
The paper presents a novel two-stage approach with Object-AnchorNet and ContactDM, enabling generalization to unseen objects and high-quality 4D HOI synthesis from limited datasets.
Findings
Achieves state-of-the-art results on OMOMO and 3D-FUTURE datasets.
Demonstrates strong generalization to unseen objects.
Produces high-fidelity, temporally coherent 4D HOI sequences.
Abstract
While diffusion models and large-scale motion datasets have advanced text-driven human motion synthesis, extending these advances to 4D human-object interaction (HOI) remains challenging, mainly due to the limited availability of large-scale 4D HOI datasets. In our study, we introduce GenHOI, a novel two-stage framework aimed at achieving two key objectives: 1) generalization to unseen objects and 2) the synthesis of high-fidelity 4D HOI sequences. In the initial stage of our framework, we employ an Object-AnchorNet to reconstruct sparse 3D HOI keyframes for unseen objects, learning solely from 3D HOI datasets, thereby mitigating the dependence on large-scale 4D HOI datasets. Subsequently, we introduce a Contact-Aware Diffusion Model (ContactDM) in the second stage to seamlessly interpolate sparse 3D HOI keyframes into densely temporally coherent 4D HOI sequences. To enhance the quality…
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Taxonomy
TopicsSpeech and dialogue systems · Natural Language Processing Techniques · Human Motion and Animation
