To address the poor real-time performance of asymmetric gait generation and the lack of human-robot physical interaction compliance faced by hemiplegic stroke patients using lower-limb exoskeletons for rehabilitation, this article develops a lightweight quasi-direct drive knee exoskeleton and proposes an adaptive compliant control framework based on online incremental dynamic movement primitives (DMP). In terms of hardware design, the system combines a coaxially integrated quasi-direct drive module with carbon fiber links, which significantly reduces the overall system inertia and joint back-drivability impedance, providing a highly transparent physical foundation for compliant interaction. At the control strategy level, the intention perception layer utilizes an adaptive frequency oscillator to smoothly extract the motion phase of the reference side. The trajectory planning layer proposes an incremental DMP algorithm integrating a time-sharing computation mechanism. By evenly distributing complex weight update tasks across multiple control cycles, this algorithm breaks through the computational bottleneck of embedded hardware, achieving online decoupled adjustment of spatiotemporal features and waveform amplitudes of the reference gait while satisfying 1 kHz high-frequency control demands. The underlying impedance controller then converts the trajectory deviations into compliant assistive torques. Multi-condition bilateral wearing experiments (flat ground, slope climbing, varying frequency, and varying amplitude) involving 7 subjects show that the system can accurately generalize the reference side′s motion rhythm. The bilateral kinematic consistency coefficient (PCC) reaches up to 0.977 on flat ground, with a tracking error controlled at 8.30°. Residual physical interference is restricted within ±10 N·m in the transparent mode, and a synergistic assistive torque of approximately ±20 N·m is smoothly output in the assistive mode. Without interfering with the human′s inherent gait rhythm, the proposed system successfully achieves the online real-time generation and adaptive compliant tracking of asymmetric gaits.