XPose: Towards Extreme Low Light Hand Pose Estimation

Recent advances in deep learning have enabled considerable strides in hand pose estimation in well-lit conditions. However, to the best of our knowledge, there is no existing method for hand pose estimation from RGB images captured in low-light conditions. This task is highly challenging due to the overwhelming amount of noise which plague image capture in low-light conditions (<1 lux). In this paper, we propose XPose, the first method for extreme low light hand pose estimation from RGB images. We also introduce the first dataset for low light hand pose estimation consisting of ~ 120k images along with accurate hand pose labels. Our dataset consists of images captured in low light and well-lit conditions from multiple viewpoints. We propose an innovative deep learning based methodology for monocular low-light hand pose estimation using guidance from well-lit and multi-view images available in our dataset, during training time. We show that our method, using the proposed LLPose dataset, significantly outperforms existing methods for hand pose estimation both qualitatively and quantitatively in low light conditions.