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.

MOTIVATION: Molecular property prediction with deep learning has accelerated drug discovery and retrosynthesis. However, the shortage of labeled molecular data and the challenge of generalizing across the vast chemical spaces pose significant hurdles for leveraging deep learning in molecular property prediction. This study proposes a self-supervised framework designed to acquire a Simplified Molecular Input Line Entry System (SMILES) representation, which we have dubbed Simple SMILES contrastive learning (SimSon). SimSon was pre-trained using unlabeled SMILES data through contrastive learning to grasp the SMILES representations. RESULTS: Our findings demonstrate that contrastive learning with randomized SMILES enriches the ability of the model to generalize and its robustness as it captures the global semantic context at the molecular level. In downstream tasks, SimSon performs competitively when compared to graph-based methods and even outperforms them on certain benchmark datasets. These results indicate that SimSon effectively captures structural information from SMILES, exhibiting remarkable generalization and robustness. The potential applications of SimSon extend to bioinformatics and cheminformatics, encompassing areas such as drug discovery and drug-drug interaction prediction. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/lee00206/SimSon.