3D Human Motion Estimation via Motion Compression and Refinement

08/09/2020
by   Zhengyi Luo, et al.
28

We develop a technique for generating smooth and accurate 3D human pose and motion estimates from RGB video sequences. Our technique, which we call Motion Estimation via Variational Autoencoder (MEVA), decomposes a temporal sequence of human motion into a smooth motion representation using auto-encoder-based motion compression and a residual representation learned through motion refinement. This two-step encoding of human motion captures human motion in two stages: a general human motions estimation step that captures the coarse overall motion, and a residual estimation that adds back person-specific motion details. Experiments show that our method produces both smooth and accurate 3D human pose and motion estimates.

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Code Repositories

MEVA

Official implementation of ACCV 2020 paper "3D Human Motion Estimation via Motion Compression and Refinement" (Identical repo to https://github.com/KlabCMU/MEVA, will be kept in sync)


view repo

MEVA

Official implementation of ACCV 2020 paper "3D Human Motion Estimation via Motion Compression and Refinement". (Identical repo to https://github.com/ZhengyiLuo/MEVA, will be kept in sync)


view repo
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