EggHand: A Multimodal Foundation Model for Egocentric Hand Pose Forecasting

• URL: http://arxiv.org/abs/2605.07642v1
• Date: Fri, 08 May 2026 12:09:27 GMT
• Title: EggHand: A Multimodal Foundation Model for Egocentric Hand Pose Forecasting
• Abstract summary: EggHand is a foundation-model-based framework for egocentric hand pose forecasting.<n>It unifies multimodal semantic reasoning with dynamic motion modeling.<n>Our approach overcomes the brittleness of generic visual encoders under ego-motion.
• Score: 11.687067206676625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forecasting future 3D hand pose sequences from egocentric video is essential for understanding human intention and enabling embodied applications such as AR/VR assistance and human-robot interaction. However, this task remains a highly challenging problem because egocentric hand motion is driven by complex human intent, exhibits highly dexterous articulations, and is observed under drastic viewpoint shifts induced by ego-motion. In this work, we introduce EggHand, a foundation-model-based framework for egocentric hand pose forecasting that unifies multimodal semantic reasoning with dynamic motion modeling. Our approach couples an action decoder from a Vision-Language-Action (VLA) model, which captures the structured temporal dynamics of hand motion, with an egocentric video-text encoder that provides viewpoint-aware contextual information learned from large-scale first-person video. Together, these components overcome the brittleness of generic visual encoders under ego-motion and enable joint reasoning over motion, context, and high-level intent-without relying on body pose or external tracking. Experiments on the EgoExo4D dataset show that EggHand sets a new state of the art in forecasting accuracy, remains robust under severe ego-motion, and further enables controllable prediction via language-based task prompts. Project page: https://jyoun9.github.io/EggHand

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