LAMP tracks 3D human motion from moving multi-camera headsets by converting 2D detections to a unified metric 3D world frame via device localization and fitting with an end-to-end spatio-temporal transformer.
Egohumans: An egocentric 3d multi-human benchmark
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MoViD disentangles motion and view features via a view estimator and orthogonal projection with contrastive alignment to deliver viewpoint-invariant 3D pose estimation that cuts errors over 24% with 60% less data and runs at 15 FPS on edge hardware.
citing papers explorer
-
LAMP: Localization Aware Multi-camera People Tracking in Metric 3D World
LAMP tracks 3D human motion from moving multi-camera headsets by converting 2D detections to a unified metric 3D world frame via device localization and fitting with an end-to-end spatio-temporal transformer.
-
MoViD: View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement
MoViD disentangles motion and view features via a view estimator and orthogonal projection with contrastive alignment to deliver viewpoint-invariant 3D pose estimation that cuts errors over 24% with 60% less data and runs at 15 FPS on edge hardware.