Human Motion Prediction Using Manifold-Aware Wasserstein GAN - Archive ouverte HAL Access content directly
Conference Papers Year : 2021

Human Motion Prediction Using Manifold-Aware Wasserstein GAN


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. The pretrained models and the code are provided at the following link.
Fichier principal
Vignette du fichier
IEEE_FG_2021-3.pdf (647.68 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03384332 , version 1 (18-10-2021)


  • HAL Id : hal-03384332 , version 1


Baptiste Chopin, Naima Otberdout, Mohamed Daoudi, Angela Bartolo. Human Motion Prediction Using Manifold-Aware Wasserstein GAN. IEEE conference series on Automatic Face and Gesture Recognition, Dec 2021, Jodhpur (virtual), India. ⟨hal-03384332⟩
29 View
60 Download


Gmail Facebook X LinkedIn More