Large-Scale Multi-Character Interaction Synthesis

TL;DR

This paper introduces a novel deep learning pipeline for generating large-scale, coordinated multi-character interactions, addressing data scarcity and transition planning challenges in character animation.

Contribution

It proposes a conditional generative framework with a multi-character interaction space and a transition planning network, enabling scalable and transferable multi-character interaction synthesis.

Findings

Effective synthesis of multi-character interactions demonstrated

Pipeline handles dense and close interactions successfully

Applications show scalability and transferability

Abstract

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 coordinated with each other for transition. For example, a dance scenario involves characters dancing with partners and also characters coordinated to new partners based on spatial and temporal observations. We term such transitions as coordinated interactions and decompose them 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 functions that may not generalize well. While crowd simulation involves more characters, their interactions are sparse and passive. We identify two challenges to multi-character interaction synthesis, including the lack of data and the planning of transitions among close and dense interactions. Existing datasets either do not have multiple characters or do not have close and dense interactions. The planning of transitions for multi-character close and dense interactions needs both spatial and temporal considerations. We propose a conditional generative pipeline comprising a coordinatable multi-character interaction space for interaction synthesis and a transition planning network for coordinations. Our experiments demonstrate the effectiveness of our proposed pipeline for multicharacter interaction synthesis and the applications facilitated by our method show the scalability and transferability.