InterFusion: Text-Driven Generation of 3D Human-Object Interaction
<|reference_start|>InterFusion: Text-Driven Generation of 3D Human-Object Interaction: In this study, we tackle the complex task of generating 3D human-object interactions (HOI) from textual descriptions in a zero-shot text-to-3D manner. We identify and address two key challenges: the unsatisfactory outcomes of direct text-to-3D methods in HOI, largely due to the lack of paired text-interaction data, and the inherent difficulties in simultaneously generating multiple concepts with complex spatial relationships. To effectively address these issues, we present InterFusion, a two-stage framework specifically designed for HOI generation. InterFusion involves human pose estimations derived from text as geometric priors, which simplifies the text-to-3D conversion process and introduces additional constraints for accurate object generation. At the first stage, InterFusion extracts 3D human poses from a synthesized image dataset depicting a wide range of interactions, subsequently mapping these poses to interaction descriptions. The second stage of InterFusion capitalizes on the latest developments in text-to-3D generation, enabling the production of realistic and high-quality 3D HOI scenes. This is achieved through a local-global optimization process, where the generation of human body and object is optimized separately, and jointly refined with a global optimization of the entire scene, ensuring a seamless and contextually coherent integration. Our experimental results affirm that InterFusion significantly outperforms existing state-of-the-art methods in 3D HOI generation.<|reference_end|>
@article{dai2024interfusion:,
    title={InterFusion: Text-Driven Generation of 3D Human-Object Interaction},
    author={Sisi Dai, Wenhao Li, Haowen Sun, Haibin Huang, Chongyang Ma, Hui
  Huang, Kai Xu, Ruizhen Hu},
    journal={arXiv preprint arXiv:2403.15612},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15612},
    primaryClass={cs.CV}
}