DGHMesh supplies a large-scale dual-radar mmWave dataset and generalization benchmark for human mesh reconstruction, together with the mmPTM multi-radar fusion model that reports strong accuracy and cross-configuration performance.
Deep learning-based human pose estimation: A survey
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
EnDKF combines ensemble Kalman filtering with directional statistics and unit quaternions to achieve lower pose tracking error than raw measurements in synthetic constant-velocity tests and FoundationPose-based head tracking.
citing papers explorer
-
DGHMesh: A Large-scale Dual-radar mmWave Dataset and Generalization-focused Benchmark for Human Mesh Reconstruction
DGHMesh supplies a large-scale dual-radar mmWave dataset and generalization benchmark for human mesh reconstruction, together with the mmPTM multi-radar fusion model that reports strong accuracy and cross-configuration performance.
-
Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
EnDKF combines ensemble Kalman filtering with directional statistics and unit quaternions to achieve lower pose tracking error than raw measurements in synthetic constant-velocity tests and FoundationPose-based head tracking.