UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction,
  Forecasting, and Generation
Authors: name: Chaitanya Patel, name: Hiroki Nakamura, name: Yuta Kyuragi, name: Kazuki Kozuka, name: Juan Carlos Niebles, name: Ehsan Adeli

Egocentric human motion generation and forecasting with scene-context is
crucial for enhancing AR/VR experiences, improving human-robot interaction,
advancing assistive technologies, and enabling adaptive healthcare solutions by
accurately predicting and simulating movement from a first-person perspective.
However, existing methods primarily focus on third-person motion synthesis with
structured 3D scene contexts, limiting their effectiveness in real-world
egocentric settings where limited field of view, frequent occlusions, and
dynamic cameras hinder scene perception. To bridge this gap, we introduce
Egocentric Motion Generation and Egocentric Motion Forecasting, two novel tasks
that utilize first-person images for scene-aware motion synthesis without
relying on explicit 3D scene. We propose UniEgoMotion, a unified conditional
motion diffusion model with a novel head-centric motion representation tailored
for egocentric devices. UniEgoMotion's simple yet effective design supports
egocentric motion reconstruction, forecasting, and generation from first-person
visual inputs in a unified framework. Unlike previous works that overlook scene
semantics, our model effectively extracts image-based scene context to infer
plausible 3D motion. To facilitate training, we introduce EE4D-Motion, a
large-scale dataset derived from EgoExo4D, augmented with pseudo-ground-truth
3D motion annotations. UniEgoMotion achieves state-of-the-art performance in
egocentric motion reconstruction and is the first to generate motion from a
single egocentric image. Extensive evaluations demonstrate the effectiveness of
our unified framework, setting a new benchmark for egocentric motion modeling
and unlocking new possibilities for egocentric applications.