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.

翻译：我们研究了在极低光照图像中进行人形姿态估计的任务。由于在真实的低光照条件下收集精确标注的图像数据非常具有挑战性，并且分别严重影响预测质量，使得该任务变得困难。为了解决第一个问题，我们开发了一套专用相机系统，构建了一个新的数据集，其中包括真实的低光照图像以及准确的姿态标签。由于我们的相机系统，我们的数据集中的每个低光照图像都与一个对齐的充分照明图像相对应，这使得标注姿态信息变得准确，并且可以在训练过程中作为特权信息。我们还提出了一种新的模型和新的训练策略，充分利用特权信息，学习对光照条件不敏感的表示。我们的方法在真实的极低光照图像上展示了出色的性能，广泛的分析验证了我们的模型和数据集的全部贡献。