Year: 2025
Type: article
Abstract: Human Motion Prediction is developed to project human motion in the future frames.With lots of papers tend to predict the future motion via Recurrent Neural Network, Multi-Layer Perceptron, or Graph Convolution Network, many complicated motions have been predicted more accurately.However, most existing methods have met drawbacks in long-term predictions.To make the long-term prediction better, we propose a network called DAFCN, using Fast Fourier Convolution to optimize the result on long-term human motion prediction.Our approach also makes advantages of the Motion Attention Model to abstract the short-term useful information.Furthermore, our approach attempts to make prediction in short-term and long-term paths, which will be mixed in the output layers.Currently, our approach is experimented on Human3.6M,which demonstrate a better result on prediction.The code is available at: https://github.com/Kiramei/DAFCN".
Author: Chengjie Lu
Institution: Shenzhen University
Language: English
Topic: Human Motion and Animation
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410105907