3D animation of human body movement is quite challenging as it involves using a huge setup with several motion trackers all over the persons body to track the movements of every limb. This is time- consuming and may cause the person discomfort in wearing exoskeleton body suits with motion sensors. In this work, we present a trivial yet effective solution to generate simple 3D animation of human movement of multiple persons from a 2D video using deep learning. Although significant improvement has been achieved recently in 3D human pose estimation, most of the prior works work well in case of single person pose estimation and multi-person pose estimation is still a challenging problem. In this work, we firstly propose a supervised multi-person 3D pose estimation and animation framework namely AnimePose for a given input RGB video sequence. The pipeline of the proposed system consists of various modules: i) Multi-Person 2D pose estimation, ii) Depth Map estimation, iii) Lifting 2D poses to 3D poses, iv) Person trajectory prediction and human pose tracking. Our proposed system produces comparable results on previous state-of-the-art 3D multi-person pose estimation methods on publicly available dataset MuPoTS-3D dataset and it also outperforms previous competing human pose tracking methods by a significant margin of 11.7% performance gain on MOTA score on Posetrack 2018 dataset.

人体运动的3D动画非常具有挑战性，因为它涉及到在整个人体上使用带有多个运动跟踪器的大型装置来跟踪每个肢体的运动。这很耗时，并且可能导致穿着带运动传感器的外骨骼紧身衣着的人感到不适。在这项工作中，我们提出了一个简单而有效的解决方案，可以使用深度学习从2D视频生成人类多人活动的简单3D动画。尽管最近在3D人体姿势估计方面已实现了重大改进，但是在单人姿势估计的情况下，大多数现有技术都可以正常工作，而多人姿势估计仍然是一个具有挑战性的问题。在这项工作中，我们首先提出了一个有监督的多人3D姿势估计和动画框架，即AnimePose给定的输入RGB视频序列。所提出的系统的管线包括各种模块：i）多人2D姿势估计，ii）深度图估计，iii）将2D姿势提升到3D姿势，iv）人的轨迹预测和人的姿势跟踪。我们提出的系统在可公开获得的数据集MuPoTS-3D数据集上，可以与先前的最新3D多人姿势估计方法产生可比的结果，并且在性能上比以前的竞争性人类姿势跟踪方法高出11.7％。 Posetrack 2018数据集上的MOTA分数。