OmniLab
News
- 2024-10-30 - Code released on Github.
- 2024-08-25 - Paper accepted by ECCV 2024 Synthetic Data for Computer Vision Workshop.
- 2024-08-15 - OmniLab dataset released with images, bounding boxes and keypoints.
- 2024-04-24 - NToP paper v2 is published on arXiv.
Sample images of each action in OmniLab
Abstract
Please download and sign the dataset release agreement, and send it to Jingrui Yu for access of the dataset. The dataset is for non-commercial usage only.
In order to evaluate the effectiveness of NToP in real-world scenarios, we collect a new dataset OmniLab with a top-view omnidirectional camera, mounted on the ceiling of two different rooms (bedroom, living room) at 2.5 m height. Five actors (3 males, 2 females) perform 15 actions from CMU-MoCap database (brooming, cleaning windows, down and get up, drinking, fall-on-face, in chair and stand up, pull object, push object, rugpull, turn left, turn right, upbend from knees, upbend from waist, up from ground, walk, walk-old-man) in two rooms with varying clothes. The recorded action length is 2.5 s, which results in 60 images for each scene at a frame rate of 24 FPS. The position of the camera is fixed and the resolution of the images is 1200 by 1200 pixels. A total of 4800 frames are collected. All annotations of 17 keypoints conforming to COCO conventions are estimated through a keypoint detector and subsequently refined by four different humans in two loops to ensure high annotation quality. Bottom figure shows a few examples from OmniLab with person bounding boxes and keypoint annotations.
Paper
Code
BibTeX
If you use the data set in your work, please don't forget to cite:
@misc{yu2024ntop,
title={NToP: NeRF-Powered Large-scale Dataset Generation for 2D and 3D Human Pose Estimation in Top-View Fisheye Images},
author={Jingrui Yu and Dipankar Nandi and Roman Seidel and Gangolf Hirtz},
year={2024},
eprint={2402.18196},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2402.18196},
}