  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.
