---
title: "Learning 3-D Human Pose Estimation from Catadioptric Videos"
authors:
  - "Chenchen Liu"
  - "Yongzhi Li"
  - "Kangqi Ma"
  - "Duo Zhang"
  - "Peijun Bao"
  - "Yadong Mu"
doi: "10.24963/ijcai.2021/118"
url: "https://ijcai.org/proceedings/2021/118"
pdf: "https://ijcai.org/proceedings/2021/0118.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "852-859"
track: "Main Track"
keywords:
  - "Computer Vision: 2D and 3D Computer Vision"
  - "Computer Vision: Video: Events, Activities and Surveillance"
---

# Learning 3-D Human Pose Estimation from Catadioptric Videos

Chenchen Liu, Yongzhi Li, Kangqi Ma, Duo Zhang, Peijun Bao, Yadong Mu

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 852-859. DOI: [10.24963/ijcai.2021/118](https://doi.org/10.24963/ijcai.2021/118)

## Abstract

3-D human pose estimation is a crucial step for understanding human actions. However, reliably capturing precise 3-D position of human joints is non-trivial and tedious. Current models often suffer from the scarcity of high-quality 3-D annotated training data. In this work, we explore a novel way of obtaining gigantic 3-D human pose data without manual annotations. In catedioptric videos (\emph{e.g.}, people dance before a mirror), the camera records both the original and mirrored human poses, which provides cues for estimating 3-D positions of human joints. Following this idea, we crawl a large-scale Dance-before-Mirror (DBM) video dataset, which is about 24 times larger than existing Human3.6M benchmark. Our technical insight is that, by jointly harnessing the epipolar geometry and human skeleton priors, 3-D joint estimation can boil down to an optimization problem over two sets of 2-D estimations. To our best knowledge, this represents the first work that collects high-quality 3-D human data via catadioptric systems. We have conducted comprehensive experiments on cross-scenario pose estimation and visualization analysis. The results strongly demonstrate the usefulness of our proposed DBM human poses.