  Optical motion capture (mocap) requires accurately reconstructing the human
body from retroreflective markers, including pose and shape. In a typical mocap
setting, marker labeling is an important but tedious and error-prone step.
Previous work has shown that marker labeling can be automated by using a
structured template defining specific marker placements, but this places
additional recording constraints. We propose to relax these constraints and
solve for Unstructured Unlabeled Optical (UUO) mocap. Compared to the typical
mocap setting that either labels markers or places them w.r.t a structured
layout, markers in UUO mocap can be placed anywhere on the body and even on one
specific limb (e.g., right leg for biomechanics research), hence it is of more
practical significance. It is also more challenging. To solve UUO mocap, we
exploit a monocular video captured by a single RGB camera, which does not
require camera calibration. On this video, we run an off-the-shelf method to
reconstruct and track a human individual, giving strong visual priors of human
body pose and shape. With both the video and UUO markers, we propose an
optimization pipeline towards marker identification, marker labeling, human
pose estimation, and human body reconstruction. Our technical novelties include
multiple hypothesis testing to optimize global orientation, and marker
localization and marker-part matching to better optimize for body surface. We
conduct extensive experiments to quantitatively compare our method against
state-of-the-art approaches, including marker-only mocap and video-only human
body/shape reconstruction. Experiments demonstrate that our method resoundingly
outperforms existing methods on three established benchmark datasets for both
full-body and partial-body reconstruction.
