3D Human Motion Estimation via Motion Compression and Refinement

Code

• github.com/ZhengyiLuo/MEVA
  Officialpytorch★ 107
• github.com/KlabCMU/MEVA
  pytorch★ 6

Abstract

We develop a technique for generating smooth and accurate 3D human pose and motion estimates from RGB video sequences. Our method, 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 motion 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.

Tasks

Reproductions