Modeling and generating 3D human-object interactions from text is crucial for applications in AR, XR, and gaming. Existing approaches often rely on score distillation from text-to-image models, but their results suffer from the Janus problem and do not follow text prompts faithfully due to the scarcity of high-quality interaction data. We introduce Hoi3DGen, a framework that generates high-quality textured meshes of human-object interaction that follow the input interaction descriptions precisely. We first curate realistic and high-quality interaction data leveraging multimodal large language models, and then create a full text-to-3D pipeline, which achieves orders-of-magnitude improvements in interaction fidelity. Our method surpasses baselines by 4-15x in text consistency and 3-7x in 3D model quality, exhibiting strong generalization to diverse categories and interaction types, while maintaining high-quality 3D generation.
@article{arxiv.2603.12126,
title = {Hoi3DGen: Generating High-Quality Human-Object-Interactions in 3D},
author = {Agniv Sharma and Xianghui Xie and Tom Fischer and Eddy Ilg and Gerard Pons-Moll},
journal= {arXiv preprint arXiv:2603.12126},
year = {2026}
}