Stochastic Multi-Person 3D Motion Forecasting
Sirui Xu Yu-Xiong Wang Liang-Yan Gui
This paper aims to deal with the ignored real-world complexities 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; we instantiate it with different generative models,
including generative adversarial networks and diffusion models, and 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.