  Existing 2D human pose estimation research predominantly concentrates on
well-lit scenarios, with limited exploration of poor lighting conditions, which
are a prevalent aspect of daily life. Recent studies on low-light pose
estimation require the use of paired well-lit and low-light images with ground
truths for training, which are impractical due to the inherent challenges
associated with annotation on low-light images. To this end, we introduce a
novel approach that eliminates the need for low-light ground truths. Our
primary novelty lies in leveraging two complementary-teacher networks to
generate more reliable pseudo labels, enabling our model achieves competitive
performance on extremely low-light images without the need for training with
low-light ground truths. Our framework consists of two stages. In the first
stage, our model is trained on well-lit data with low-light augmentations. In
the second stage, we propose a dual-teacher framework to utilize the unlabeled
low-light data, where a center-based main teacher produces the pseudo labels
for relatively visible cases, while a keypoints-based complementary teacher
focuses on producing the pseudo labels for the missed persons of the main
teacher. With the pseudo labels from both teachers, we propose a
person-specific low-light augmentation to challenge a student model in training
to outperform the teachers. Experimental results on real low-light dataset
(ExLPose-OCN) show, our method achieves 6.8% (2.4 AP) improvement over the
state-of-the-art (SOTA) method, despite no low-light ground-truth data is used
in our approach, in contrast to the SOTA method. Our code will be available
at:https://github.com/ayh015-dev/DA-LLPose.
