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 temporal network architecture and show that adversarial training, at the sequence level, produces kinematically plausible motion sequences without in-the-wild ground-truth 3D labels.
The code for the PyTorch implementation of VIBE can be found on @mkocabas‘ GitHub Repo. If you’d like to try out the implementation there is also a CoLab version. If you’d like to see some example output check out this video.
Written by Rebecca Minich, Product Analyst, Data Science at Google. Opinions expressed are solely my own and do not express the views or opinions of my employer.
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