AnimePose: Multi-person 3D pose estimation and animation

• URL: http://arxiv.org/abs/2002.02792v1
• Date: Thu, 6 Feb 2020 11:11:56 GMT
• Title: AnimePose: Multi-person 3D pose estimation and animation
• Authors: Laxman Kumarapu and Prerana Mukherjee
• Abstract summary: 3D animation of humans in action is quite challenging as it involves using a huge setup with several motion trackers all over the person's 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. We present a solution to generate 3D animation of multiple persons from a 2D video using deep learning.
• Score: 9.323689681059504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D animation of humans in action is quite challenging as it involves using a huge setup with several motion trackers all over the person's 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 3D animation 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) Person detection and segmentation, ii) Depth Map estimation, iii) Lifting 2D to 3D information for person localization 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 datasets MuCo-3DHP and MuPoTS-3D datasets and it also outperforms previous state-of-the-art human pose tracking methods by a significant margin of 11.7% performance gain on MOTA score on Posetrack 2018 dataset.

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