Human hands are central to environmental interactions, motivating increasing research on dexterous robotic manipulation. Data-driven embodied AI algorithms demand precise, large-scale, human-like manipulation sequences, which are challenging to obtain with conventional reinforcement learning or real-world teleoperation. This paper introduces ManipTrans, a novel two-stage method for efficiently transferring human bimanual skills to dexterous robotic hands in simulation. ManipTrans first pre-trains a generalist trajectory imitator for mimicking hand motion, then fine-tunes a specialist residual module for each skill under physics-based interaction constraints. This approach decouples motion imitation from physical effects, enabling efficient learning and accurate execution of complex bimanual tasks.Experiments show ManipTrans surpasses state-of-the-art methods in success rate, fidelity, and efficiency. Using ManipTrans, we transfer multiple hand-object datasets to robotic hands, creating DexManipNet, a large-scale dataset featuring previously unexplored tasks like pen capping and bottle unscrewing. DexManipNet includes 3.3K episodes of robotic manipulation and supports easy expansion with our framework, facilitating further policy training for dexterous hands and enabling real-world applications. Both code and dataset will be publicly available.