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Advances in neural fields are enablling high-fidelity capture of shape and appearance of dynamic 3D scenes. However, this capbabilities lag behind those offered by conventional representations such as 2D videos because of algorithmic challenges and the lack of large-scale multi-view real-world datasets. We address the dataset limitations with DiVa-360, a real-world 360° dynamic visual dataset that contains synchronized high-resolution and long-duration multi-view video sequences of table-scale scenes captured using a customized low-cost system with 53 cameras. It contains 21 object-centric sequences categorized by different motion types, 25 intricate hand-object interaction sequences, and 8 long-duration sequences for a total of 17.4M frames. In addition, we provide foreground-background segmentation masks, synchronized audio, and text descriptions. We benchmark the state-of-the-art dynamic neural field methods on DiVa-360 and provide insights about existing methods and future challenges on long-duration neural field capture. 

Juvenile idiopathic arthritis (JIA) is a chronic disease. During its “high disease activity (HDA)” stage, JIA can cause severe pain, and thus could seriously affect patients' physical and psychological health. Early detection of the HDA stage of JIA can reduce the damage of the disease by treating it at an early stage and alleviating the painful experience of the patients. So far, no effective cure of JIA has been found, and one major goal of disease management is to improve patients' quality of life. To this end, patients' health‐related quality of life (HRQOL) scores are routinely collected over time from JIA patients. In this paper, we demonstrate that a new statistical methodology called dynamic screening system (DySS) is effective for early detection of the HDA stage of JIA. By this approach, a patient's HRQOL scores are monitored sequentially, and a signal is given by DySS once the longitudinal pattern of the scores is found to be significantly different from the pattern of patients with low disease activity. Dimension reduction of the observed HRQOL scores and the corresponding impact on the performance of DySS are also discussed.