# Synthesizing 3D Gait Data with Personalized Walking Style and Appearance
## 5 Experimental Results
### Subject Shape Variation  
Figure 5 shows the changes in human shape when semantic parameters are altered. For cloth adaptation, we tested a different number of human shape vertices \(k\) around each cloth vertex. As we can see from Figure 6, the adaptation gets better with the increase in \(k\); however, the discrepancy is negligible when \(k\geq 6\). We also compare the different blending operators and presented the results in Figure 7, which reveals better effects are achieved with dual quaternion blending.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{
\begin{tabular}{c} **Feature** \\ **Vector** \\ \end{tabular} } & \multirow{3}{*}{**Metric**} & \multicolumn{6}{c}{**Content Embedding Distances**} & \multicolumn{6}{c}{**ID Embedding Distances**} \\ \cline{3-14}  & & & **Walk** & & **Run** & & **W-R** & & **P1** & & **P2** & & **P1-P2** \\ \cline{2-14}  & & **Dist \(D_{W}\)** & **Std** & **Dist \(D_{R}\)** & **Std** & **Dist \(D_{W-R}\)** & \(D_{C}\)** & **Dist \(D_{P1}\)** & **Std** & **Dist \(D_{P2}\)** & **Std** & **Dist \(D_{P1-P2}\)** & \(D_{P}\)** \\ \hline \multirow{3}{*}{\(\mathbf{C}_{a}\)} & \(\mathbf{L}_{1}\) & 53.22 & 12.25 & 24.41 & 15.16 & 95.25 & 1.27 & 13.79 & 13.98 & 11.53 & 11.17 & 62.67 & 2.48 \\ \cline{2-14}  & \(\mathbf{L}_{2}\) & 21.88 & 6.61 & 8.17 & 4.84 & 25.89 & 0.86 & 6.82 & 7.23 & 5.36 & 5.07 & 47.12 & 3.87 \\ \cline{2-14}  & \(\mathbf{L}_{m}\) & 2.77 & 0.92 & 1.93 & 1.22 & 3.24 & 0.81 & 1.12 & 0.92 & 1.61 & 1.06 & 2.91 & 1.07 \\ \hline \multirow{3}{*}{\(\mathbf{C}_{r}\)} & \(\mathbf{L}_{1}\) & 73.67 & 26.63 & 38.10 & 18.32 & 100.12 & 1.00 & 40.22 & 26.11 & 34.95 & 17.20 & 79.77 & 1.30 \\ \cline{2-14}  & \(\mathbf{L}_{2}\) & 43.50 & 18.78 & 24.41 & 12.62 & 60.23 & 0.89 & 34.20 & 21.90 & 29.70 & 16.57 & 51.06 & 0.80 \\ \cline{2-14}  & \(\mathbf{L}_{m}\) & 1.98 & 0.57 & 0.96 & 0.50 & 3.20 & 1.09 & 0.89 & 0.55 & 0.78 & 0.35 & 2.43 & 1.46 \\ \hline \multirow{3}{*}{\(\mathbf{H}_{a}\)} & \(\mathbf{L}_{1}\) & 48.11 & 10.83 & 23.26 & 14.38 & 71.18 & 1.00 & 10.71 & 10.64 & 6.64 & 8.52 & 49.76 & 2.87 \\ \cline{2-14}  & \(\mathbf{L}_{2}\) & 17.30 & 4.82 & 6.73 & 4.06 & 42.67 & 1.78 & 5.27 & 5.56 & 3.08 & 3.86 & 36.27 & 4.34 \\ \cline{2-14}  & \(\mathbf{L}_{m}\) & 3.26 & 1.04 & 2.78 & 1.79 & 3.24 & 0.54 & 1.10 & 0.91 & 0.90 & 1.03 & 2.93 & 1.47 \\ \hline \multirow{3}{*}{\(\mathbf{H}_{r}\)} & \(\mathbf{L}_{1}\) & 64.90 & 16.94 & 31.67 & 12.84 & 71.86 & 0.74 & 33.93 & 16.91 & 29.39 & 11.19 & 65.28 & 1.03 \\ \cline{2-14}  & \(\mathbf{L}_{2}\) & 28.79 & 8.79 & 15.40 & 7.07 & 42.08 & 0.95 & 22.37 & 11.10 & 18.21 & 7.27 & 37.49 & 0.92 \\ \cline{2-14}  & \(\mathbf{L}_{m}\) & 2.89 & 0.71 & 1.52 & 0.61 & 3.24 & 0.73 & 1.38 & 0.59 & 1.15 & 0.38 & 2.70 & 1.07 \\ \hline \multirow{3}{*}{\(\mathbf{G}_{a}\)} & \(\mathbf{L}_{1}\) & 575.62 & 178.39 & 621.93 & 361.30 & 1423.82 & 1.19 & 112.60 & 98.07 & 42.51 & 32.51 & 719.86 & 4.64 \\ \cline{2-14}  & \(\mathbf{L}_{2}\) & **250.40** & **74.48** & **242.08** & **147.29** & **953.01** & **1.94** & **71.18** & **61.89** & **27.94** & **21.78** & **494.09** & **4.98** \\ \cline{2-14}  & \(\mathbf{L}_{m}\) & 2.16 & 0.74 & 3.17 & 1.96 & 3.23 & 0.61 & 0.82 & 0.91 & 0.33 & 0.33 & 2.90 & 2.52 \\ \hline \multirow{3}{*}{\(\mathbf{G}_{r}\)} & \(\mathbf{L}_{1}\) & 2215.04 & 1167.85 & 1352.18 & 704.60 & 1954.28 & 0.55 & 1603.36 & 1019.97 & 1081.91 & 535.17 & 1008.38 & 0.38 \\ \cline{2-14}  & \(\mathbf{L}_{2}\) & 1302.38 & 676.76 & 830.79 & 421.47 & 1312.16 & 0.62 & 1276.79 & 786.57 & 833.78 & 404.82 & 823.57 & 0.39 \\ \cline{1-1} \cline{2-14}  & \(\mathbf{L}_{m}\) & 2.10 & 0.95 & 1.14 & 0.62 & 3.09 & 0.95 & 1.22 & 0.65 & 0.91 & 0.42 & 0.70 & 0.33 \\ \hline \hline \end{tabular} Note: For the two feature vectors \(\mathbf{u}\) and \(\mathbf{v}\), \(\mathbf{L}_{1}=\parallel\mathbf{u}-\mathbf{v}\parallel\), \(\mathbf{L}_{2}=\parallel\mathbf{u}-\mathbf{v}\parallel^{2