We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in $360^\circ$ with diverse appearance and detailed geometry using only in-the-wild unstructured images for training.
To bridge the gap, we present mRI, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors.
The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day.
Movement disorders, such as Parkinson's disease, affect more than 10 million people worldwide.
The typical approach is training a HAR classifier offline with known users and then using the same classifier for new users.