Wireless-based gesture recognition provides an effective input method for exergames. However, previous works in wireless-based gesture recognition systems mainly recognize one primary user's gestures. In the multi-player scenario, the mutual interference between users makes it difficult to predict multiple players' gestures individually. To address this challenge, we propose a flexible FMCW-radar-based system, RFDual, which enables real-time cross-domain gesture sequence recognition for two players. To eliminate the mutual interference between users, we extract a new feature type, biased range-velocity spectrum (BRVS), which only depends on a target user. We then propose customized preprocessing methods (cropping and stationary component removal) to produce environment-independent and position-independent inputs. To enhance RFDual's resistance to unseen users and articulating speeds, we design effective data augmentation methods, sequence concatenating, and randomizing. RFDual is evaluated with a dataset containing only unseen gesture sequences and achieves a gesture error rate of 1.41%. Extensive experimental results show the impressive robustness of RFDual for data in new domains, including new users, articulating speeds, positions, and environments. These results demonstrate the great potential of RFDual in practical applications like two-player exergames and gesture/activity recognition for drivers and passengers in the cab.