Table I:

Mean squared error (μ error) for estimation and prediction of 3D pose in degrees along with the overall standard error of mean (σ_μ) and the standard error of different timesteps, ages, and speed of motion for the test data. The top part of the table compares estimation models and the bottom part compares prediction models. In these comparisons we also tested our model trained without any masked slices in the sequences, referred to as the “baseline”, our second baseline trained with masked slice sequences but without the split heads and the loss function explained in Section II-F (referred to as ”masked bl.”) and our ”full model” trained with both masked slices and the split loss function. Significant reduction in both estimation and prediction errors was achieved by our full trained model compared to baselines and all other compared models. Low standard errors show that our model performed consistently, and was robust to variations in data, timesteps, GA, and the speed of motion.

Model	μ error	σμ	σμ time	σμ age	σμ speed
VGG16	129.33	11.74	3.72	3.48	9.51
Resnet18	82.60	5.76	3.55	1.31	3.34
Our baseline	20.19	2.57	1.21	2.23	2.06
Our masked bl.	9.10	2.31	1.11	1.92	2.45
Our full model	3.55	0.22	0.17	0.05	0.23
directLSTM	103.20	3.09	0.97	13.52	5.80
Zero velocity	74.14	1.09	0.86	1.77	1.32
Auto regressive	96.77	1.66	0.69	1.83	2.17
Our baseline	33.51	2.35	1.17	1.23	1.11
Our masked bl.	11.28	1.28	1.17	0.23	0.51
Our full model	8.07	0.72	0.42	0.39	0.59