Wearable exoskeletons can augment human strength and reduce muscle fatigue during specific tasks. However, developing personalized and task-generalizable assistance algorithms remains a critical challenge. To address this, a meta-imitation learning approach is proposed. This approach leverages a task-specific neural network to predict human elbow joint movements, enabling effective assistance while enhancing generalization to new scenarios. To accelerate data collection, full-body keypoint motions are extracted from publicly available RGB video and motion-capture datasets across multiple tasks, and subsequently retargeted in simulation. Elbow flexion trajectories generated in simulation are then used to train the task-specific neural network within the model-agnostic meta-learning (MAML) framework, which allows the network to rapidly adapt to novel tasks and unseen users with only a few gradient updates. The adapted network outputs personalized references tracked by a gravity-compensated PD controller to ensure stable assistance. Experimental results demonstrate that the exoskeleton significantly reduces both muscle activation and metabolic cost for new users performing untrained tasks, compared to performing without exoskeleton assistance. These findings suggest that the proposed framework effectively improves task generalization and user adaptability for wearable exoskeleton systems.