EgoGrasp: World-Space Hand-Object Interaction Estimation from Egocentric Videos

• URL: http://arxiv.org/abs/2601.01050v1
• Date: Sat, 03 Jan 2026 03:08:48 GMT
• Title: EgoGrasp: World-Space Hand-Object Interaction Estimation from Egocentric Videos
• Authors: Hongming Fu, Wenjia Wang, Xiaozhen Qiao, Shuo Yang, Zheng Liu, Bo Zhao,
• Abstract summary: We propose EgoGrasp, the first method to reconstruct world-space hand-object interactions (W-HOI) from egocentric monocular videos with dynamic cameras in the wild.<n>In experiments, we prove our method achieving state-of-the-art performance in W-HOI reconstruction.
• Score: 25.047225764745978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose EgoGrasp, the first method to reconstruct world-space hand-object interactions (W-HOI) from egocentric monocular videos with dynamic cameras in the wild. Accurate W-HOI reconstruction is critical for understanding human behavior and enabling applications in embodied intelligence and virtual reality. However, existing hand-object interactions (HOI) methods are limited to single images or camera coordinates, failing to model temporal dynamics or consistent global trajectories. Some recent approaches attempt world-space hand estimation but overlook object poses and HOI constraints. Their performance also suffers under severe camera motion and frequent occlusions common in egocentric in-the-wild videos. To address these challenges, we introduce a multi-stage framework with a robust pre-process pipeline built on newly developed spatial intelligence models, a whole-body HOI prior model based on decoupled diffusion models, and a multi-objective test-time optimization paradigm. Our HOI prior model is template-free and scalable to multiple objects. In experiments, we prove our method achieving state-of-the-art performance in W-HOI reconstruction.

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