Human Pose Estimation in Extremely Low-light Conditions

Project Page | Paper

This repo is the official implementation of [CVPR 2023] paper: "Human Pose Estimation in Extremely Low-light Conditions".

Human Pose Estimation in Extremely Low-light Conditions
Sohyun Lee^1*, Jaesung Rim^1*, Boseung Jeong¹, Geonu Kim¹, Byungju Woo², Haechan Lee¹, Sunghyun Cho¹, Suha Kwak¹
POSTECH¹ ADD²
CVPR 2023

Overview

We study human pose estimation in extremely low-light images. This task is challenging due to the difficulty of collecting real low-light images with accurate labels, and severely corrupted inputs that degrade prediction quality significantly. To address the first issue, we develop a dedicated camera system and build a new dataset of real lowlight images with accurate pose labels. Thanks to our camera system, each low-light image in our dataset is coupled with an aligned well-lit image, which enables accurate pose labeling and is used as privileged information during training. We also propose a new model and a new training strategy that fully exploit the privileged information to learn representation insensitive to lighting conditions. Our method demonstrates outstanding performance on real extremely low-light images, and extensive analyses validate that both of our model and dataset contribute to the success.

Citation

If you find our code or paper useful, please consider citing our paper:

@inproceedings{lee2023human,
  title={Human pose estimation in extremely low-light conditions},
  author={Lee, Sohyun and Rim, Jaesung and Jeong, Boseung and Kim, Geonu and Woo, Byungju and Lee, Haechan and Cho, Sunghyun and Kwak, Suha},
  booktitle={Proceedings of the {IEEE/CVF} Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2023}
}

Dataset

Our Project Page

Installation

This repository is developed and tested on

• Ubuntu 20.04
• Conda 4.9.2
• CUDA 11.4
• Python 3.7.11
• PyTorch 1.9.0

Environment Setup

• Required environment is presented in the 'exlpose.yaml' file
• Clone this repo

~$ git clone https://github.com/sohyun-l/ExLPose
~$ cd ExLPose
~/ExLPose$ conda env create --file exlpose.yaml
~/ExLPose$ conda activate exlpose.yaml

Training

(exlpose) ~/ExLPose$ cd pytorch-cpn/256.192.model
(exlpose) ~/ExLPose/pytorch-cpn/256.192.model$ python train.py

Our Model

BEST_MODEL_PATH = './Final_model.pth.tar'