Multi-view egocentric dynamic scene reconstruction holds significant research value for applications in holographic documentation of social interactions. However, existing reconstruction datasets focus on static multi-view or single-egocentric view setups, lacking multi-view egocentric datasets for dynamic scene reconstruction. Therefore, we present MultiEgo, the first multi-view egocentric dataset for 4D dynamic scene reconstruction. The dataset comprises five canonical social interaction scenes: meetings, performances, and a presentation. Each scene provides five authentic egocentric videos captured by participants wearing AR glasses. We design a hardware-based data acquisition system and processing pipeline, achieving sub-millisecond temporal synchronization across views, coupled with accurate pose annotations. Experiment validation demonstrates the practical utility and effectiveness of our dataset for free-viewpoint video (FVV) applications, establishing MultiEgo as a foundational resource for advancing multi-view egocentric dynamic scene reconstruction research.
@article{arxiv.2512.11301,
title = {MultiEgo: A Multi-View Egocentric Video Dataset for 4D Scene Reconstruction},
author = {Bate Li and Houqiang Zhong and Zhengxue Cheng and Qiang Hu and Qiang Wang and Li Song and Wenjun Zhang},
journal= {arXiv preprint arXiv:2512.11301},
year = {2025}
}