Towards Robust and Smooth 3D Multi-Person Pose Estimation from Monocular
  Videos in the Wild
Sungchan Park Eunyi You Inhoe Lee Joonseok Lee
3D pose estimation is an invaluable task in computer vision with various
practical applications. Especially, 3D pose estimation for multi-person from a
monocular video (3DMPPE) is particularly challenging and is still largely
uncharted, far from applying to in-the-wild scenarios yet. We pose three
unresolved issues with the existing methods: lack of robustness on unseen views
during training, vulnerability to occlusion, and severe jittering in the
output. As a remedy, we propose POTR-3D, the first realization of a
sequence-to-sequence 2D-to-3D lifting model for 3DMPPE, powered by a novel
geometry-aware data augmentation strategy, capable of generating unbounded data
with a variety of views while caring about the ground plane and occlusions.
Through extensive experiments, we verify that the proposed model and data
augmentation robustly generalizes to diverse unseen views, robustly recovers
the poses against heavy occlusions, and reliably generates more natural and
smoother outputs. The effectiveness of our approach is verified not only by
achieving the state-of-the-art performance on public benchmarks, but also by
qualitative results on more challenging in-the-wild videos. Demo videos are
available at https://www.youtube.com/@potr3d.