Name
MPFSIR
Description
In recent years, multi-person pose forecasting has gained significant attention due to its potential applications in various fields such as computer vision, robotics, sports analysis, and human-robot interaction. In this paper, we propose a novel deep learning model for multi-person pose forecasting called MPFSIR (multi-person pose forecasting and social interaction recognition) that achieves comparable results with state-of-the-art models, but with up to 30 times fewer parameters. In addition, the model includes a social interaction prediction component to model and predict interactions between individuals. We evaluate our model on three benchmark datasets: 3DPW, CMU-Mocap, and MuPoTS-3D, compare it with state-of-the-art methods, and provide an ablation study to analyze the impact of the different model components. Experimental results show the effectiveness of MPFSIR in accurately predicting future poses and capturing social interactions. Furthermore, we introduce the metric MW-MPJPE to evaluate the perform
Publication title
MPFSIR: An Effective Multi-Person Pose Forecasting Model with Social Interaction Recognition
Publication authors
Romeo Sajina, Marina Ivasic Kos
Publication venue and year
IEEE Access, 2023
Publication URL
https://ieeexplore.ieee.org/document/10210381
Language(s)
Python
Hardware
Website
Source code URL
https://github.com/RomeoSajina/MPFSIR
Creation date
2023-06-20 08:23:11
| Metric | 80/100 ms | 160/240 ms | 320/500 ms | 400/640 ms | 560/900 ms |
|---|---|---|---|---|---|
| PoseTrack VAM | None | None | None | None | None |
| PoseTrack VIM | None | None | None | None | None |
| 3DPW VIM | 11.483091777385079 | 25.466354653307597 | 54.68011990423479 | 70.5741671539113 | 101.45548528798807 |