Hand tracking in XR serves as a fundamental interaction mechanism, as it allows users to directly interact with virtual content. Accurate 3D hand pose estimation is essential in scenarios involving dynamic hand motions, such as gaming, sports, and virtual musical instruments. These dynamic hand motions often result in motion blur when the hand moves faster than the frame rate of the cameras, making pose estimation challenging. The state of the art methods for 3D hand pose estimation uses deep learning that requires large amounts of data with 3D hand pose ground truth. However, most of the existing publicly available hand pose datasets are captured from static or slowly moving hands that do not contain any explicit motion blur. While techniques such as using short exposure times with higher frame rates have been employed to reduce motion blur, they still pose limitations for developing accurate hand pose estimation algorithms in the presence of fast motion. To address these challenges, firstly, we introduce a new dataset, EgoBlur, consisting of egocentric hand videos with real blur captured from a prototype Head-mounted headset. Our dataset contains$\sim 100 \mathrm{k}$images along with accurate and temporally consistent 3D hand pose ground truth. Secondly, we propose EgoBlurNet, a deep learning model capable of estimating 3D hand keypoints from blurry egocentric images by employing a teacher-student paradigm. Experimental results demonstrate that our method provides reliable and accurate 3D hand pose for blurred hand images compared to existing methods, especially in realistic dynamic XR scenarios.

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.