EgoTwin: Dreaming Body and View in First Person

TL;DR

EgoTwin is a novel framework that jointly generates egocentric videos and human motion by modeling head-centric motion and causal interactions, addressing the underexplored area of first-person view synthesis.

Contribution

The paper introduces EgoTwin, a diffusion transformer-based model with head-centric motion representation and causal interaction mechanisms for egocentric video and motion generation.

Findings

EgoTwin effectively aligns camera trajectories with human head motion.

The framework captures causal interplay between visual dynamics and motion.

Experiments show superior performance on a new real-world dataset.

Abstract

While exocentric video synthesis has achieved great progress, egocentric video generation remains largely underexplored, which requires modeling first-person view content along with camera motion patterns induced by the wearer's body movements. To bridge this gap, we introduce a novel task of joint egocentric video and human motion generation, characterized by two key challenges: 1) Viewpoint Alignment: the camera trajectory in the generated video must accurately align with the head trajectory derived from human motion; 2) Causal Interplay: the synthesized human motion must causally align with the observed visual dynamics across adjacent video frames. To address these challenges, we propose EgoTwin, a joint video-motion generation framework built on the diffusion transformer architecture. Specifically, EgoTwin introduces a head-centric motion representation that anchors the human motion to the head joint and incorporates a cybernetics-inspired interaction mechanism that explicitly captures the causal interplay between video and motion within attention operations. For comprehensive evaluation, we curate a large-scale real-world dataset of synchronized text-video-motion triplets and design novel metrics to assess video-motion consistency. Extensive experiments demonstrate the effectiveness of the EgoTwin framework.