Sohyun Lee*,     Jaesung Rim*,     Boseung Jeong,     Geonu Kim,     ByungJu Woo,     Haechan Lee,     Sunghyun Cho†,     Suha Kwak†
* Equal contribution. † Corresponding authors.
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 low-light 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.
|LLFlow + Baseline-all||35.2||20.1||8.3||22.1||65.1|
|LIME + Baseline-all||38.3||25.6||12.5||26.6||63.0|