  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.
