EgoWorld: Translating Exocentric View to Egocentric View using Rich Exocentric Observations

• URL: http://arxiv.org/abs/2506.17896v1
• Date: Sun, 22 Jun 2025 04:21:48 GMT
• Title: EgoWorld: Translating Exocentric View to Egocentric View using Rich Exocentric Observations
• Authors: Junho Park, Andrew Sangwoo Ye, Taein Kwon,
• Abstract summary: We introduce EgoWorld, a novel framework that reconstructs an egocentric view from rich exocentric observations.<n>Our approach reconstructs a point cloud from estimated exocentric depth maps, reprojects it into the egocentric perspective, and then applies diffusion-based inpainting to produce dense, semantically coherent egocentric images.<n>EgoWorld achieves state-of-the-art performance and demonstrates robust generalization to new objects, actions, scenes, and subjects.
• Score: 4.252119151012245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Egocentric vision is essential for both human and machine visual understanding, particularly in capturing the detailed hand-object interactions needed for manipulation tasks. Translating third-person views into first-person views significantly benefits augmented reality (AR), virtual reality (VR) and robotics applications. However, current exocentric-to-egocentric translation methods are limited by their dependence on 2D cues, synchronized multi-view settings, and unrealistic assumptions such as necessity of initial egocentric frame and relative camera poses during inference. To overcome these challenges, we introduce EgoWorld, a novel two-stage framework that reconstructs an egocentric view from rich exocentric observations, including projected point clouds, 3D hand poses, and textual descriptions. Our approach reconstructs a point cloud from estimated exocentric depth maps, reprojects it into the egocentric perspective, and then applies diffusion-based inpainting to produce dense, semantically coherent egocentric images. Evaluated on the H2O and TACO datasets, EgoWorld achieves state-of-the-art performance and demonstrates robust generalization to new objects, actions, scenes, and subjects. Moreover, EgoWorld shows promising results even on unlabeled real-world examples.

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