  In this paper, we present the novel task of estimating the extrinsic
parameters of a virtual camera relative to a real camera in exercise videos
with a mirror. This task poses a significant challenge in scenarios where the
views from the real and mirrored cameras have no overlap or share salient
features. To address this issue, prior knowledge of a human body and 2D joint
locations are utilized to estimate the camera extrinsic parameters when a
person is in front of a mirror. We devise a modified eight-point algorithm to
obtain an initial estimation from 2D joint locations. The 2D joint locations
are then refined subject to human body constraints. Finally, a RANSAC algorithm
is employed to remove outliers by comparing their epipolar distances to a
predetermined threshold. MirrorCalib achieves a rotation error of 1.82{\deg}
and a translation error of 69.51 mm on a collected real-world dataset, which
outperforms the state-of-art method.
