3D Human Motion Estimation via Motion Compression and Refinement
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.
PDF AbstractCode
Tasks
Datasets
Human3.6M
3DPW
AMASS
MPI-INF-3DHP
Results from the Paper
Ranked #14 on
3D Human Pose Estimation
on 3DPW
(Acceleration Error metric, using extra
training data)
