Two Views Are Better than One: Monocular 3D Pose Estimation with
  Multiview Consistency
Christian Keilstrup Ingwersen Anders Bjorholm Dahl Janus Nørtoft Jensen Morten Rieger Hannemose
Deducing a 3D human pose from a single 2D image or 2D keypoints is inherently
challenging, given the fundamental ambiguity wherein multiple 3D poses can
correspond to the same 2D representation. The acquisition of 3D data, while
invaluable for resolving pose ambiguity, is expensive and requires an intricate
setup, often restricting its applicability to controlled lab environments. We
improve performance of monocular human pose estimation models using multiview
data for fine-tuning. We propose a novel loss function, multiview consistency,
to enable adding additional training data with only 2D supervision. This loss
enforces that the inferred 3D pose from one view aligns with the inferred 3D
pose from another view under similarity transformations. Our consistency loss
substantially improves performance for fine-tuning with no available 3D data.
Our experiments demonstrate that two views offset by 90 degrees are enough to
obtain good performance, with only marginal improvements by adding more views.
Thus, we enable the acquisition of domain-specific data by capturing activities
with off-the-shelf cameras, eliminating the need for elaborate calibration
procedures. This research introduces new possibilities for domain adaptation in
3D pose estimation, providing a practical and cost-effective solution to
customize models for specific applications. The used dataset, featuring
additional views, will be made publicly available.