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In this paper, we deal with the problem of synthesizing novel motions of standing-up martial arts such as kickboxing, karate, and taekwondo performed by a pair of humanlike characters while reflecting their interactions. Adopting an example-based paradigm, we address three nontrivial issues embedded in this problem: motion modeling, interaction modeling, and motion synthesis. For the first issue, we present a semiautomatic motion-labeling scheme based on force-based motion segmentation and learning-based action classification. We also construct a pair of motion transition graphs, each of which represents an individual motion stream. For the second issue, we propose a scheme for capturing the interactions between two players. A dynamic Bayesian network is adopted to build a motion transition model on top of the coupled motion transition graph that is constructed from an example motion stream. For the last issue, we provide a scheme for synthesizing a novel sequence of coupled motions, guided by the motion transition model. Although the focus of the present work is on martial arts, we believe that the framework of the proposed approach can be conveyed to other two-player motions as well.