Generating large-scale multi-character interactions is a challenging and important task in character animation. Multi-character interactions involve not only natural interactive motions but also characters are coordinated with each other for transition. For example, a dancing interaction involves characters that are dancing with partners and also characters that are coordinated to transit to new partners based on spatial and temporal observations. We term such transitions as coordinated interaction for multi-character interactions and decompose it into interaction synthesis and transition planning. Previous methods of single character animation do not consider interactions that are critical for multiple characters. Deep learning based interaction synthesis usually focuses on two characters and does not consider transition planning. Optimization-based interaction synthesis relies on manually designing objective function that may not generalize well. While crowd simulation involves more characters, their interactions are sparse and passive. We identify two challenges towards multi-character interaction synthesis, including the lack of data and the transition planning with close and dense interactions. Existing datasets either do not have multiple characters or do not have close and dense interactions. Planning transitions for multi-character close and dense interactions requires both spatial and temporal consideration. We propose a conditional generative pipeline, including a coordinatable multi-character interaction space for interaction synthesis and a transition planning network to plan transitions. Our experiments demonstrate the effectiveness of our proposed pipeline for multi-character interaction synthesis and the applications facilitated by our method show the scalability and transferability.