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Guide To VIBE: Video Inference for 3D Human Body Pose and Shape Estimation

VIBE - Video Inference for 3D Human Body Pose and Shape Estimation. It uses CNNs, RNNs(GRU) and GANs along with a self-attention layer to achieve its state-of-the-art results.
Pose estimation is now a greater research area. Until now developments have been made based on human body 2D keypoint annotations. Most of the solutions have been around a single image or 2D motion and significantly less on the 3D motion as it involves more challenges. The primary challenge being less ground truth training 3D annotated data. Some of these 3D motion researches that have come across are not satisfactory and suffer from many drawbacks. Also, these methods are mostly frame-based, which increase error rates.  In February 2020 (later updated in April) PhD students Muhammed Kocabas, Nikos Athanasiou, and director Michael J. Black at Max Planck Institute for Intelligent Systems represented their paper to CVPR named “VIBE: Video Inference for Human Body Pose and Shape Est
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Picture of Jayita Bhattacharyya
Jayita Bhattacharyya
Machine learning and data science enthusiast. Eager to learn new technology advances. A self-taught techie who loves to do cool stuff using technology for fun and worthwhile.
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