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.

为了解决现有数据集的限制，我们引入了 LaserHuman，这是一个开创性的数据集，旨在革新 Scene-Text-to-Motion 研究。LaserHuman 通过包含 3D 环境中的真实人类动作，自由形式的自然语言描述，室内外场景的混合以及动态、不断变化的场景而脱颖而出。多样的采集数据和丰富的注释为条件运动生成研究提供了巨大机遇，也可以促进实际应用的开发。此外，为了生成语义一致且物理可行的人体动作，我们提出了一个简单但有效的多条件扩散模型，在现有数据集上达到了最先进的性能。