Stochastic Multi-Person 3D Motion Forecasting

• URL: http://arxiv.org/abs/2306.05421v1
• Date: Thu, 8 Jun 2023 17:59:09 GMT
• Title: Stochastic Multi-Person 3D Motion Forecasting
• Authors: Sirui Xu, Yu-Xiong Wang, Liang-Yan Gui
• Abstract summary: We deal with the ignored real-world complexities in prior work on human motion forecasting. Our framework is general; we instantiate it with different generative models. Our approach produces diverse and accurate multi-person predictions, significantly outperforming the state of the art.
• Score: 21.915057426589744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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