# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 5 Experiments
### Qualitative Evaluation
#### 5.1.3 Comparisons to Methods Found in the Literature  
Figure 14 shows a qualitative comparison between the proposed approach and more recent state-of-the-art methods [36; 37; 38; 62]. As the figure shows, all five of the methods give visually satisfying transfer results in terms of long-term temporal consistency. However, the representation of texture pattern from the style reference image varies differently, and the details (e.g., object contours) are preserved with artefacts. For example, the representative mosaic texture patterns from the style image basically do not appear in any of the results of previous four methods [36; 37; 38; 62], but the colour information does. In addition, [PERSON] et al. [62] and [PERSON] et al. [37] produced distorted results with severe artefacts in which object contours are distorted in the zoom-ins (please see the zoom-ins in the third and fourth row). [PERSON] et al. [38] introduced strange vertical artefacts (please see zoom-ins in the fifth row) into their results. [PERSON] et al. [36] produced unfaithful colour information in their results compared to the other methods. In contrast, our results preserve the mosaic texture patterns better and object contours do not have distortions. Figure 12: The effect of image sharpness. The top rows are original video frames, the middle rows are outputs without Sharpness Losses, and the bottom rows are outputs with Sharpness Losses. The red rectangles indicate the difference of image sharpness. Figure 13: The effect of temporal consistency. The per-frame processing methods are [PERSON] et al. [10] and [PERSON] et al. [13]. The red and green rectangles indicate the discontinuous texture appearances. Figure 15 shows a comparison between our approach and [PERSON] et al. [35]. The method proposed by [PERSON] et al. [35] learns a linear transformation matrix to minimize the difference between the covariance of the transformed content features and style features, which serves as a second-order statistics transformation of the reference image onto the content image in prior methods [9, 10]. The linear transformation is highly efficient but causes less stylistic texture to be present in transformed videos. For instance, the mosaic texture patterns in the style reference image in Figure 14 do not appear in their transferred video frames, which increases the temporal consistency among adjacent frames but leads to the appearance of less artistic texture. However, our method preserves temporal consistency (c.f. last table) and texture patterns better than theirs (c.f. zoom-ins). Figure 14: Comparison to latest state-of-the-art methods [36, 37, 38, 62] on bike-packing scene from DAVIS 2017 dataset [66]. The red rectangles indicate the differences in long-term temporal consistency results. Figure 16 shows the comparison between our approach and [PERSON] et al. [30] in a large motion and strong occlusion case. The network-based video style transfer approach proposed by [PERSON] et al. [30] fails to produce consistent texture appearances around traceable flow areas (white areas) as their method is not able to correct such flow errors, while our approach corrects these traceable flow errors via the proposed mask techniques, and generates consistent texture appearances in the given contexts. Figure 16: Comparison to [PERSON] et al. [30] on Ambush_4 scene from MPI Sintel dataset [66]. The per-frame processing method for both methods is [PERSON] et al. [10]. The rectangles indicate the difference in the consistent texture appearances in a few adjacent stabilization results. Orange rectangles show the texture appearances around large occlusion boundaries (including flow traceable errors) by [PERSON] et al. [30] are discontinuous with context, while purple boxes demonstrate that our textures are consistent with context. Please view our Supplementary Video. Figure 15: Comparison to [PERSON] et al. [35] on Soapbox scene from DAVIS 2017 dataset [66]. Both the red and green rectangles indicate the differences in two adjacent stabilization results.