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• W3216644997 abstract "We present an approach for 3D global human mesh recovery from monocular videos recorded with dynamic cameras. Our approach is robust to severe and long-term occlusions and tracks human bodies even when they go outside the camera's field of view. To achieve this, we first propose a deep generative motion infiller, which autoregressively infills the body motions of occluded humans based on visible motions. Additionally, in contrast to prior work, our approach reconstructs human meshes in consistent global coordinates even with dynamic cameras. Since the joint reconstruction of human motions and camera poses is underconstrained, we propose a global trajectory predictor that generates global human trajectories based on local body movements. Using the predicted trajectories as anchors, we present a global optimization framework that refines the predicted trajectories and optimizes the camera poses to match the video evidence such as 2D keypoints. Experiments on challenging indoor and in-the-wild datasets with dynamic cameras demonstrate that the proposed approach outperforms prior methods significantly in terms of motion infilling and global mesh recovery." @default.
• W3216644997 created "2021-12-06" @default.
• W3216644997 creator A5037322163 @default.
• W3216644997 creator A5056503617 @default.
• W3216644997 creator A5066945976 @default.
• W3216644997 creator A5067824015 @default.
• W3216644997 creator A5081199756 @default.
• W3216644997 date "2021-12-02" @default.
• W3216644997 modified "2023-09-24" @default.
• W3216644997 title "GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras" @default.
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