  We propose a new deep learning network that introduces a deeper CNN channel
filter and constraints as losses to reduce joint position and motion errors for
3D video human body pose estimation. Our model outperforms the previous best
result from the literature based on mean per-joint position error, velocity
error, and acceleration errors on the Human 3.6M benchmark corresponding to a
new state-of-the-art mean error reduction in all protocols and motion metrics.
Mean per joint error is reduced by 1%, velocity error by 7% and acceleration by
13% compared to the best results from the literature. Our contribution
increasing positional accuracy and motion smoothness in video can be integrated
with future end to end networks without increasing network complexity. Our
model and code are available at https://vnmr.github.io/
  Keywords: 3D, human, image, pose, action, detection, object, video, visual,
supervised, joint, kinematic
