UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction, Forecasting, and Generation
Chaitanya Patel, Hiroki Nakamura, Yuta Kyuragi, Kazuki Kozuka, Juan Carlos Niebles, Ehsan Adeli
2025-08-06
Summary
This paper talks about UniEgoMotion, a single AI model that can recreate, predict, and generate first-person motion movements using images taken from the person's own viewpoint.
What's the problem?
The problem is that understanding and predicting how people move from their own perspective is difficult because motions are complex and change quickly, and existing methods often focus on separate tasks, not unifying them.
What's the solution?
UniEgoMotion solves this by using a motion diffusion model that takes first-person images to create smooth, realistic motion sequences, predict future movements, and generate new motions all within one system.
Why it matters?
This matters because better egocentric motion models can help improve virtual reality, robotics, sports training, and any technology that needs to understand or generate human movements from a first-person view.
Abstract
A unified conditional motion diffusion model, UniEgoMotion, is introduced for egocentric motion generation and forecasting using first-person images, achieving state-of-the-art performance and generating motion from a single image.