  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.
