  In this paper, we introduce the new task of reconstructing 3D human pose from
a single image in which we can see the person and the person's image through a
mirror. Compared to general scenarios of 3D pose estimation from a single view,
the mirror reflection provides an additional view for resolving the depth
ambiguity. We develop an optimization-based approach that exploits mirror
symmetry constraints for accurate 3D pose reconstruction. We also provide a
method to estimate the surface normal of the mirror from vanishing points in
the single image. To validate the proposed approach, we collect a large-scale
dataset named Mirrored-Human, which covers a large variety of human subjects,
poses and backgrounds. The experiments demonstrate that, when trained on
Mirrored-Human with our reconstructed 3D poses as pseudo ground-truth, the
accuracy and generalizability of existing single-view 3D pose estimators can be
largely improved.
