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Synthesizing multi-character interactions is a challenging task due to thecomplex and varied interactions between the characters. In particular, precisespatiotemporal alignment between characters is required in generating closeinteractions such as dancing and fighting. Existing work in generatingmulti-character interactions focuses on generating a single type of reactivemotion for a given sequence which results in a lack of variety of the resultantmotions. In this paper, we propose a novel way to create realistic humanreactive motions which are not presented in the given dataset by mixing andmatching different types of close interactions. We propose a ConditionalHierarchical Generative Adversarial Network with Multi-Hot Class Embedding togenerate the Mix and Match reactive motions of the follower from a given motionsequence of the leader. Experiments are conducted on both noisy (depth-based)and high-quality (MoCap-based) interaction datasets. The quantitative andqualitative results show that our approach outperforms the state-of-the-artmethods on the given datasets. We also provide an augmented dataset withrealistic reactive motions to stimulate future research in this area. The codeis available at https://github.com/Aman-Goel1/IMM

Interaction Mix and Match: Synthesizing Close Interaction using Conditional Hierarchical GAN with Multi-Hot Class Embedding