LaserHuman: Language-guided Scene-aware Human Motion Generation in Free Environment

• URL: http://arxiv.org/abs/2403.13307v2
• Date: Thu, 21 Mar 2024 13:06:49 GMT
• Title: LaserHuman: Language-guided Scene-aware Human Motion Generation in Free Environment
• Authors: Peishan Cong, Ziyi Wang, Zhiyang Dou, Yiming Ren, Wei Yin, Kai Cheng, Yujing Sun, Xiaoxiao Long, Xinge Zhu, Yuexin Ma,
• Abstract summary: 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. We propose a multi-conditional diffusion model, which is simple but effective, achieving state-of-the-art performance on existing datasets.
• Score: 27.38638713080283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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