LaserHuman: Language-guided Scene-aware Human Motion Generation in Free Environment
<|reference_start|>LaserHuman: Language-guided Scene-aware Human Motion Generation in Free Environment: Language-guided scene-aware human motion generation has great significance for entertainment and robotics. In response to the limitations of existing datasets, we introduce LaserHuman, a pioneering dataset engineered to revolutionize Scene-Text-to-Motion research. LaserHuman stands out with its inclusion of genuine human motions within 3D environments, unbounded free-form natural language descriptions, a blend of indoor and outdoor scenarios, and dynamic, ever-changing scenes. Diverse modalities of capture data and rich annotations present great opportunities for the research of conditional motion generation, and can also facilitate the development of real-life applications. Moreover, to generate semantically consistent and physically plausible human motions, we propose a multi-conditional diffusion model, which is simple but effective, achieving state-of-the-art performance on existing datasets.<|reference_end|>
@article{cong2024laserhuman:,
    title={LaserHuman: Language-guided Scene-aware Human Motion Generation in Free
  Environment},
    author={Peishan Cong, Ziyi Wang, Zhiyang Dou, Yiming Ren, Wei Yin, Kai Cheng,
  Yujing Sun, Xiaoxiao Long, Xinge Zhu, Yuexin Ma},
    journal={arXiv preprint arXiv:2403.13307},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13307},
    primaryClass={cs.CV}
}