Title: Human Pose Estimation in the Dark. Abstract: 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 a near-perfectly aligned well-lit image, which enables accurate pose labeling and is used as privileged information during training. We also propose a new model that fully exploits the privileged information to learn representation insensitive to lighting conditions. Our model demonstrates outstanding performance on real extremely low-light images, and extensive analyses validate that both of our dataset and model contribute to the success.