We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

We propose a method to reconstruct global human trajectories from videos in the wild. Our
optimization method decouples the camera and human motion, which allows us to place …

We have recently seen tremendous progress in diffusion advances for generating realistic
human motions. Yet, they largely disregard the multi-human interactions. In this paper, we …

Estimating 3D human motion from an egocentric video sequence plays a critical role in
human behavior understanding and has various applications in VR/AR. However, naively …

Tracking body pose on-the-go could have powerful uses in fitness, mobile gaming, context-
aware virtual assistants, and rehabilitation. However, users are unlikely to buy and wear …

Predicting human motion is critical for assistive robots and AR/VR applications, where the
interaction with humans needs to be safe and comfortable. Meanwhile, an accurate …

Understanding social interactions from egocentric views is crucial for many applications,
ranging from assistive robotics to AR/VR. Key to reasoning about interactions is to …

We propose a self-supervised algorithm to learn representations from egocentric video data.
Recently, significant efforts have been made to capture humans interacting with their own …

Procedure learning involves identifying the key-steps and determining their logical order to
perform a task. Existing approaches commonly use third-person videos for learning the …

Understanding human tasks through video observations is an essential capability of
intelligent agents. The challenges of such capability lie in the difficulty of generating a …