# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 5 Experiments
### Quantitative Evaluation
#### 5.2.4 User Study  
Furthermore, we carried out a user study experiment for comparison. To be specific, we used five videos and three style images to present fifteen video clips from seven methods to 50 participants. The demographic consisted of 27 males and 23 females, aged from 22 to 40. Then, we asked for their preferences over three aspects for each video: temporal consistency, style transformation effect (including the preservation of colour and texture patterns) and overall preference. In total, we received 150 votes for each video and 2250 votes from the 50 subjects. Figure 19 shows that our method obtained the majority of votes for style transformation effect and overall preference, but took second place in terms of temporal consistency compared to [PERSON] et al. [36]. These results are consistent with Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**MPI Sintel Dataset + Style1**} & \multicolumn{4}{c}{**Davis 2017 Dataset + Style2**} \\ \cline{2-10}  & **S1** & **S2** & **S3** & **S4** & **S5** & **S1** & **S2** & **S3** & **S4** & **S5** \\ \hline [PERSON] et al. [10] & 8.15 & 7.91 & 11.96 & 13.06 & 14.50 & 12.73 & 12.63 & 13.01 & 13.62 & 14.11 \\ Ours & 3.13 & 3.56 & 6.82 & 7.88 & 8.25 & 4.08 & 4.42 & 4.85 & 4.92 & 5.13 \\ \hline [PERSON] et al. [13] & 9.16 & 10.11 & 14.51 & 14.32 & 14.44 & 15.50 & 14.52 & 15.43 & 15.77 & 16.28 \\ Ours & 3.46 & 4.39 & 7.66 & 8.28 & 8.38 & 5.00 & 4.99 & 5.86 & 5.90 & 6.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: **Stability errors (\(\times 10^{-2}\)) of per-frame processing methods and our approach on five testing videos in each dataset.** Style1: Candy, Style2: Mondrian. In MPI Sintel Dataset, there are five scenes used in this table, and they are S1: alley_1, S2: bamboo_2, S3: cave_4, S4: market_5, S5: temple_3. In Davis 2017 Dataset, there are five scenes used and they are S1: dance-flare, S2: car-turn, S3: parkour, S4: soapbox, S5: stroller. The best results are shown underlined.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Stability Errors (\(\downarrow\))**} & \multicolumn{4}{c}{**SIFID (\(\downarrow\))**} \\ \cline{2-9}  & **Style1** & **Style2** & **Style3** & **Mean** & **Style1** & **Style2** & **Style3** & **Mean** \\ \hline [PERSON] et al. [30] & 8.56 & 5.06 & 6.84 & 6.82 & 1.47 & 0.82 & 1.04 & 1.1117 \\ [PERSON] et al. [35] & 8.19 & 5.06 & 6.13 & 6.46 & 1.70 & 0.84 & 1.30 & 1.2772 \\ [PERSON] et al. [62] & 9.72 & 5.26 & 6.89 & 7.29 & 2.09 & 1.00 & 1.62 & 1.5695 \\ [PERSON] et al. [37] & 9.52 & 5.74 & 7.49 & 7.58 & 1.95 & 1.17 & 1.57 & 1.5632 \\ [PERSON] et al. [38] & 8.17 & 5.34 & 5.47 & 6.33 & 1.97 & 1.04 & 1.63 & 1.5481 \\ Ours & 7.76 & 4.95 & 6.15 & 6.29 & 1.52 & 0.67 & 0.99 & 1.0615 \\ [PERSON] et al. [36] & 6.68 & \(\overline{3.57}\) & \(\overline{5.75}\) & \(\overline{5.34}\) & \(\overline{1.94}\) & 1.76 & 1.40 & 1.7002 \\ \hline \hline \end{tabular}
\end{table}
Table 3: **Quantitative comparison of stability errors (\(\times 10^{-2}\)) and SIFID metrics of six state-of-the-art methods and ours on 20 videos in MPI Sintel dataset.** The groundtruth flow and occlusion masks are provided by MPI Sintel dataset. All the image resolutions are \(1024\times 436\). The baseline model of [PERSON] et al. [30] and ours is [PERSON] et al. [10]. Style1: Candy, Style2: The_Scream, Style3: Woman With A Hat. The best two results are highlighted in one underline and two underlines.