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

TL;DR

This paper introduces a novel Conditional Hierarchical GAN with Multi-Hot Class Embedding to synthesize diverse, realistic multi-character interactions, including unseen motions, outperforming existing methods on multiple datasets.

Contribution

It presents a new generative model that enables mixing and matching different close interactions to produce realistic reactive motions not present in the training data.

Findings

Outperforms state-of-the-art methods on multiple datasets

Generates realistic reactive motions including unseen types

Provides an augmented dataset for future research

Abstract

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