Human Motion Prediction Using Manifold-Aware Wasserstein GAN

• URL: http://arxiv.org/abs/2105.08715v1
• Date: Tue, 18 May 2021 17:56:10 GMT
• Title: Human Motion Prediction Using Manifold-Aware Wasserstein GAN
• Authors: Baptiste Chopin, Naima Otberdout, Mohamed Daoudi, Angela Bartolo
• Abstract summary: We build a manifold-aware Wasserstein generative adversarial model that captures the temporal and spatial dependencies of human motion. Our approach outperforms the state-of-the-art on CMU MoCap and Human 3.6M datasets.
• Score: 4.771549505875783
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human motion prediction aims to forecast future human poses given a prior pose sequence. The discontinuity of the predicted motion and the performance deterioration in long-term horizons are still the main challenges encountered in current literature. In this work, we tackle these issues by using a compact manifold-valued representation of human motion. Specifically, we model the temporal evolution of the 3D human poses as trajectory, what allows us to map human motions to single points on a sphere manifold. To learn these non-Euclidean representations, we build a manifold-aware Wasserstein generative adversarial model that captures the temporal and spatial dependencies of human motion through different losses. Extensive experiments show that our approach outperforms the state-of-the-art on CMU MoCap and Human 3.6M datasets. Our qualitative results show the smoothness of the predicted motions.

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