  Synthesizing multi-character interactions is a challenging task due to the
complex and varied interactions between the characters. In particular, precise
spatiotemporal alignment between characters is required in generating close
interactions such as dancing and fighting. Existing work in generating
multi-character interactions focuses on generating a single type of reactive
motion for a given sequence which results in a lack of variety of the resultant
motions. In this paper, we propose a novel way to create realistic human
reactive motions which are not presented in the given dataset by mixing and
matching different types of close interactions. We propose a Conditional
Hierarchical Generative Adversarial Network with Multi-Hot Class Embedding to
generate the Mix and Match reactive motions of the follower from a given motion
sequence of the leader. Experiments are conducted on both noisy (depth-based)
and high-quality (MoCap-based) interaction datasets. The quantitative and
qualitative results show that our approach outperforms the state-of-the-art
methods on the given datasets. We also provide an augmented dataset with
realistic reactive motions to stimulate future research in this area. The code
is available at https://github.com/Aman-Goel1/IMM
