Stochastic Multi-Person 3D Motion Forecasting

Sirui Xu, Yu-Xiong Wang, Liangyan Gui

Keywords: stochastic forecasting, multi-person 3D motion, dual-level generative modeling

TL;DR: We introduce a new task of stochastic multi-person 3D motion forecasting, and propose a dual-level generative modeling framework to address this task.

Abstract: This paper aims to deal with the ignored real-world complexity in prior work on human motion forecasting, emphasizing the social properties of multi-person motion, the diversity of motion and social interactions, and the complexity of articulated motion. To this end, we introduce a novel task of stochastic multi-person 3D motion forecasting. We propose a dual-level generative modeling framework that separately models independent individual motion at the local level and social interactions at the global level. Notably, this dual-level modeling mechanism can be achieved within a shared generative model, through introducing learnable latent codes that represent intents of future motion and switching the codes' modes of operation at different levels. Our framework is general, and we instantiate it with various multi-person forecasting models. Extensive experiments on CMU-Mocap, MuPoTS-3D, and SoMoF benchmarks show that our approach produces diverse and accurate multi-person predictions, significantly outperforming the state of the art.

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