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

Di Wen, Lei Qi, Kunyu Peng, Kailun Yang, Fei Teng, Ao Luo, Jia Fu, Yufan Chen, Ruiping Liu, Yitian Shi, M. Saquib Sarfraz, Rainer Stiefelhagen

Keywords: Microgravity, Action Recognition, Vision-Language Understanding

TL;DR: MicroG-4M is the first benchmark for human action and scene understanding in microgravity, offering clips, captions, and QA pairs to reveal domain gaps and guide robust vision-language models.

Abstract:

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 with $13{,}261$ action annotations covering $50$ actions, $1{,}238$ context-rich captions, and over $7{,}000$ question–answer pairs on astronaut activities and scene understanding. MicroG-4M aims to support three core tasks: fine-grained multi-label action recognition, temporal video captioning, and visual question answering, thereby 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/MicroG-4M.

Primary Area: datasets and benchmarks

Submission Number: 16618