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An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation
Rongchang Xie, Chunyu Wang, Wenjun Zeng +1
Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images…
AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild
Zhe Zhang, Chunyu Wang, Weichao Qiu +2
Occlusion is probably the biggest challenge for human pose estimation in the wild. Typical solutions often rely on intrusive sensors such as IMUs to detect occluded joints. To make…
Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach
Zhe Zhang, Chunyu Wang, Wenhu Qin +1
We propose to estimate 3D human pose from multi-view images and a few IMUs attached at person's limbs. It operates by firstly detecting 2D poses from the two signals, and then lift…
VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment
Hanyue Tu, Chunyu Wang, Wenjun Zeng
We present an approach to estimate 3D poses of multiple people from multiple camera views. In contrast to the previous efforts which require to establish cross-view correspondence…
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
Yifu Zhang, Chunyu Wang, Xinggang Wang +2
Multi-object tracking (MOT) is an important problem in computer vision which has a wide range of applications. Formulating MOT as multi-task learning of object detection and re-ID…
MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation
Rongchang Xie, Chunyu Wang, Yizhou Wang
Cross view feature fusion is the key to address the occlusion problem in human pose estimation. The current fusion methods need to train a separate model for every pair of cameras…