# Human Pose Estimation in Monocular Omnidirectional Top-View Images

arXiv: https://arxiv.org/abs/2304.08186 · HTML: https://mlsanity.com/paper/2304.08186/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 49 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [VirtualHome: Simulating Household Activities via Programs](https://mlsanity.com/paper/1806.07011/) (2018)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [Building Generalizable Agents with a Realistic and Rich 3D Environment](https://mlsanity.com/paper/1801.02209/) (2018)
- [InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset](https://mlsanity.com/paper/1809.00716/) (2018)
- [xR-EgoPose: Egocentric 3D Human Pose from an HMD Camera](https://mlsanity.com/paper/1907.10045/) (2019)
- [Mo2Cap2: Real-time Mobile 3D Motion Capture with a Cap-mounted Fisheye Camera](https://mlsanity.com/paper/1803.05959/) (2018)
- [Estimating Egocentric 3D Human Pose in Global Space](https://mlsanity.com/paper/2104.13454/) (2021)
- [Synthesizing Training Images for Boosting Human 3D Pose Estimation](https://mlsanity.com/paper/1604.02703/) (2016)
- [SelfPose: 3D Egocentric Pose Estimation from a Headset Mounted Camera](https://mlsanity.com/paper/2011.01519/) (2020)
- [ElderSim: A Synthetic Data Generation Platform for Human Action Recognition in Eldercare Applications](https://mlsanity.com/paper/2010.14742/) (2020)
- [EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras (Extended Abstract)](https://mlsanity.com/paper/1701.00142/) (2016)
- [DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders](https://mlsanity.com/paper/2108.08557/) (2021)
- [Learning to Train with Synthetic Humans](https://mlsanity.com/paper/1908.00967/) (2019)
- [Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning](https://mlsanity.com/paper/2011.05719/) (2020)
- [OmniFlow: Human Omnidirectional Optical Flow](https://mlsanity.com/paper/2104.07960/) (2021)
- [Large Area 3D Human Pose Detection Via Stereo Reconstruction in Panoramic Cameras](https://mlsanity.com/paper/1907.00534/) (2019)
- [OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes](https://mlsanity.com/paper/2204.06846/) (2022)

## Built on by (most cited first, 3 of 3)

- [Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data](https://mlsanity.com/paper/2307.06737/) (2023)
- [NToP: NeRF-Powered Large-scale Dataset Generation for 2D and 3D Human Pose Estimation in Top-View Fisheye Images](https://mlsanity.com/paper/2402.18196/) (2024)
- [FlyPose: Towards Robust Human Pose Estimation From Aerial Views](https://mlsanity.com/paper/2601.05747/) (2026)

## Abstract (arXiv)

Human pose estimation (HPE) with convolutional neural networks (CNNs) for indoor monitoring is one of the major challenges in computer vision. In contrast to HPE in perspective views, an indoor monitoring system can consist of an omnidirectional camera with a field of view of 180deg to detect the pose of a person with only one sensor per room. To recognize human pose, the detection of keypoints is an essential upstream step. In our work we propose a new dataset for training and evaluation of CNNs for the task of keypoint detection in omnidirectional images. The training dataset, THEODORE+, consists of 50,000 images and is created by a 3D rendering engine, where humans are randomly walking through an indoor environment. In a dynamically created 3D scene, persons move randomly with simultaneously moving omnidirectional camera to generate synthetic RGB images and 2D and 3D ground truth. For evaluation purposes, the real-world PoseFES dataset with two scenarios and 701 frames with up to eight persons per scene was captured and annotated. We propose four training paradigms to finetune or re-train two top-down models in MMPose and two bottom-up models in CenterNet on THEODORE+. Beside a qualitative evaluation we report quantitative results. Compared to a COCO pretrained baseline, we achieve significant improvements especially for top-view scenes on the PoseFES dataset. Our datasets can be found at https://www.tu-chemnitz.de/etit/dst/forschung/compvision/datasets/index.php.en.