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FuturePose: Real-Time 3D Human Pose Forecasting With a RGB Camera

In this paper, we propose a novel mixed reality martial arts training system using deep learning based real-time human pose forecasting. Our training system is based on 3D pose estimation using a residual neural network with input from a RGB camera, which captures the motion of a trainer. The student wearing a head mounted display can see the virtual model of the trainer and his forecasted future pose. The pose forecasting is based on recurrent networks, to improve the learning quantity of the motion's temporal feature, we use a special lattice optical flow method for the joints movement estimation. We visualize the real-time human motion by a generated human model while the forecasted pose is shown by a red skeleton model. In our experiments, we evaluated the performance of our system when predicting 15 frames ahead in a 30-fps video (0.5s forecasting), the accuracies were acceptable since they are equal to or even outperforms some methods using depth IR cameras or fabric technologies, user studies showed that our system is helpful for beginners to understand martial arts and the usability is comfortable since the motions were captured by RGB camera. --- E. Wu and H. Koike, "FuturePose - Mixed Reality Martial Arts Training Using Real-Time 3D Human Pose Forecasting With a RGB Camera," 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 2019, pp. 1384-1392, doi: 10.1109/WACV.2019.00152.