标题： 超越地球：理解微重力环境中的人类行为与场景 显示英文标题

标题： Go Beyond Earth: Understanding Human Actions and Scenes in Microgravity Environments

摘要： 尽管在视频理解方面取得了显著进展，但大多数现有的数据集都局限于地球的重力条件。 然而，微重力会改变人类的动作、互动以及视觉语义，揭示了现实世界视觉系统的一个关键差距。 这为安全关键的空间应用中的领域鲁棒视频理解带来了挑战。 为了解决这一问题，我们引入了MicroG-4M，这是首个用于微重力环境下人体活动时空和语义理解的数据集。 该数据集基于真实世界的太空任务和电影模拟构建，包括4759个片段，涵盖50种动作、1238条上下文丰富的描述以及超过7000组关于宇航员活动和场景理解的问题-答案对。 MicroG-4M支持三个核心任务：细粒度多标签动作识别、时间视频描述以及视觉问答，能够全面评估微重力环境下的空间定位和语义推理能力。 我们使用最先进的模型建立了基线。 所有数据、标注和代码均可在https://github.com/LEI-QI-233/HAR-in-Space获取。 显示英文摘要

摘要： Despite substantial progress in video understanding, most existing datasets are limited to Earth's gravitational conditions. However, microgravity alters human motion, interactions, and visual semantics, revealing a critical gap for real-world vision systems. This presents a challenge for domain-robust video understanding in safety-critical space applications. To address this, we introduce MicroG-4M, the first benchmark for spatio-temporal and semantic understanding of human activities in microgravity. Constructed from real-world space missions and cinematic simulations, the dataset includes 4,759 clips covering 50 actions, 1,238 context-rich captions, and over 7,000 question-answer pairs on astronaut activities and scene understanding. MicroG-4M supports three core tasks: fine-grained multi-label action recognition, temporal video captioning, and visual question answering, enabling a comprehensive evaluation of both spatial localization and semantic reasoning in microgravity contexts. We establish baselines using state-of-the-art models. All data, annotations, and code are available at https://github.com/LEI-QI-233/HAR-in-Space.