Ego-1K -- A Large-Scale Multiview Video Dataset for Egocentric Vision

arXiv cs.CV / 3/17/2026

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Key Points

Ego-1K is a large-scale collection of nearly 1,000 time-synchronized egocentric multiview videos captured with a 12-camera rig around a 4-camera VR headset to advance neural 3D video synthesis and dynamic scene understanding.
The paper describes rig design, data processing, and calibration to enable benchmarking of egocentric scene reconstruction methods and to address challenges from large disparities and egomotion.
The dataset emphasizes hand motions and hand-object interactions across different settings, reflecting the difficulty existing 3D/4D view synthesis methods face with close dynamic objects.
It is publicly available on HuggingFace datasets, supporting future research as multi-camera smart glasses become more common.

Abstract

We present Ego-1K, a large-scale collection of time-synchronized egocentric multiview videos designed to advance neural 3D video synthesis and dynamic scene understanding. The dataset contains nearly 1,000 short egocentric videos captured with a custom rig with 12 synchronized cameras surrounding a 4-camera VR headset worn by the user. Scene content focuses on hand motions and hand-object interactions in different settings. We describe rig design, data processing, and calibration. Our dataset enables new ways to benchmark egocentric scene reconstruction methods, an important research area as smart glasses with multiple cameras become omnipresent. Our experiments demonstrate that our dataset presents unique challenges for existing 3D and 4D novel view synthesis methods due to large disparities and image motion caused by close dynamic objects and rig egomotion. Our dataset supports future research in this challenging domain. It is available at https://huggingface.co/datasets/facebook/ego-1k.