Multi-Object Tracking (MOT) has rapidly progressed with the development of object detection and re-identification.
Ranked #1 on Multiple Object Tracking on KITTI Tracking test
Along with the input image, we condition the top-down model on spatial context from the image in the form of body-center heatmaps.
Ranked #16 on 3D Human Pose Estimation on 3DPW (using extra training data)
Furthermore, we utilize differentiable Levenberg-Marquardt (LM) optimization to refine a pose fast and accurately by minimizing the feature-metric error between the input and rendered image representations without the need of zooming in.
Ranked #3 on 6D Pose Estimation using RGB on LineMOD
Specifically, we achieve 70. 0 AP on CrowdPose and 42. 5 AP on OCHuman test sets, a significant improvement of 2. 4 AP and 6. 5 AP over the prior art, respectively.
Ranked #1 on Pose Estimation on OCHuman
We address the issue of domain gap when making use of synthetic data to train a scene-specific object detector and pose estimator.