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• W3035551320 abstract "Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce accurate and natural motion sequences due to a lack of ground-truth 3D motion data for training. To address this problem, we propose Video Inference for Body Pose and Shape Estimation'' (VIBE), which makes use of an existing large-scale motion capture dataset (AMASS) together with unpaired, in-the-wild, 2D keypoint annotations. Our key novelty is an adversarial learning framework that leverages AMASS to discriminate between real human motions and those produced by our temporal pose and shape regression networks. We define a novel temporal network architecture with a self-attention mechanism and show that adversarial training, at the sequence level, produces kinematically plausible motion sequences without in-the-wild ground-truth 3D labels. We perform extensive experimentation to analyze the importance of motion and demonstrate the effectiveness of VIBE on challenging 3D pose estimation datasets, achieving state-of-the-art performance. Code and pretrained models are available at https://github.com/mkocabas/VIBE" @default.
• W3035551320 created "2020-06-19" @default.
• W3035551320 creator A5042004395 @default.
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• W3035551320 date "2020-06-01" @default.
• W3035551320 modified "2023-10-17" @default.
• W3035551320 title "VIBE: Video Inference for Human Body Pose and Shape Estimation" @default.
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• W3035551320 doi "https://doi.org/10.1109/cvpr42600.2020.00530" @default.
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