  We present a dataset of 1000 video sequences of human portraits recorded in
real and uncontrolled conditions by using a handheld smartphone accompanied by
an external high-quality depth camera. The collected dataset contains 200
people captured in different poses and locations and its main purpose is to
bridge the gap between raw measurements obtained from a smartphone and
downstream applications, such as state estimation, 3D reconstruction, view
synthesis, etc. The sensors employed in data collection are the smartphone's
camera and Inertial Measurement Unit (IMU), and an external Azure Kinect DK
depth camera software synchronized with sub-millisecond precision to the
smartphone system. During the recording, the smartphone flash is used to
provide a periodic secondary source of lightning. Accurate mask of the foremost
person is provided as well as its impact on the camera alignment accuracy. For
evaluation purposes, we compare multiple state-of-the-art camera alignment
methods by using a Motion Capture system. We provide a smartphone
visual-inertial benchmark for portrait capturing, where we report results for
multiple methods and motivate further use of the provided trajectories,
available in the dataset, in view synthesis and 3D reconstruction tasks.
