Human Motion Prediction Using Manifold-Aware Wasserstein GAN

05/18/2021
by   Baptiste Chopin, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/01/2022

3D Skeleton-based Human Motion Prediction with Manifold-Aware GAN

In this work we propose a novel solution for 3D skeleton-based human mot...
research
05/29/2020

Constructing Human Motion Manifold with Sequential Networks

This paper presents a novel recurrent neural network-based method to con...
research
08/20/2019

Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling

Data-driven modeling of human motions is ubiquitous in computer graphics...
research
08/11/2020

Adversarial Generative Grammars for Human Activity Prediction

In this paper we propose an adversarial generative grammar model for fut...
research
12/01/2021

Dyadic Human Motion Prediction

Prior work on human motion forecasting has mostly focused on predicting ...
research
12/19/2020

GlocalNet: Class-aware Long-term Human Motion Synthesis

Synthesis of long-term human motion skeleton sequences is essential to a...
research
04/23/2023

A Neuro-Symbolic Approach for Enhanced Human Motion Prediction

Reasoning on the context of human beings is crucial for many real-world ...

Please sign up or login with your details

Forgot password? Click here to reset