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Found 6 papers, 4 papers with code
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
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
Occluded Human Mesh Recovery
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)
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering
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
Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation
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
Adversarial Domain Randomization
Domain Randomization (DR) is known to require a significant amount of training data for good performance.
Domain Randomization for Scene-Specific Car Detection and Pose Estimation
We address the issue of domain gap when making use of synthetic data to train a scene-specific object detector and pose estimator.
