# Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability
## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.3.2 Animation Quality  
In order to compare our method with FOMM in the terms of animation quality, we performed animation generation on the TaiChiHD dataset and the VoxCeleb1 dataset. The experimental results are shown in Figures 11 and 12, respectively. The results show that the animation quality significantly improved in most cases, especially in the case of animated objects with large-scale posture change.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**TaiChiHD**} & \multicolumn{2}{c}{**VoxCeleb**} & \multicolumn{2}{c}{**MGif**} \\  & \(\mathcal{L}_{1}\) & **(AKD, MKR)** & **AED** & \(\mathcal{L}_{1}\) & **AKD** & **AED** & \(\mathcal{L}_{1}\) \\ \hline X2 Face & 0.080 & (17.65, 0.109) & 0.27 & 0.078 & 7.69 & 0.405 & - \\ FOMM & 0.061 & (6.75, 0.031) & 0.167 & 0.046 & 1.37 & 0.142 & 0.026 \\ Ours & **0.058** & **(5.84, 0.023)** & 0.170 & **0.043** & **1.30** & **0.141** & **0.026** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparing the video reconstruction evaluation indicators. FOMM and FOMM-CBAM (ours) set the number of key points to 10 (the best result is displayed in bold).  
In Figure 11, we compared the performance of our model and FOMM on the TaiChiHD dataset. The generated images in the first row show that our model produced more vivid and natural results than FOMM when the posture transformation was not large enough. In the second row, when the human body turned around, FOMM failed to locate the posture change of the human body, particularly the change in head posture, which resulted in completely incorrect human body posture in the reconstructed frame. In general, our model can transfer more complex motion postures by using the attention mechanism to extract more effective features and the multi-scale occlusion map provided by the multi-scale occlusion restoration module reconstruction frame. Hence, the generated results of our model were much better than those of FOMM. In the third row of Figure 11, we observed that the video frame generated by FOMM had a large shadow, whereas our model alleviated this problem.  
In Figure 12, the comparative results on the VoxCeleb1 dataset between our model and FOMM are provided. The images in the first and second rows show the generated video frames when the face turned to an extreme angle, such as the side face turning to the front or the front face turning to the side. FOMM lost the characteristics of the source image object, resulting in obvious errors in the generated frame. Our method used the attention mechanism to optimize the generation of the optical flow field, improving the  
Figure 11: Comparison between our method and FOMM on the TaiChiHD dataset.  
Figure 12: Comparison between our method and FOMM on the VoxCeleb1 dataset.  
accuracy of the generated optical flow field and making the motion of the generated face more consistent to the source frame. Furthermore, as shown in the third row of Figure 12, FOMM resulted in a ghosting effect (false object shadow) in the generated face, whereas our method solved this problem by gradually repairing the generated results with the multi-scale solution.  
In general, the experimental results demonstrated that our model is capable of generating the optical flow fields with higher accuracy. Furthermore, our model effectively resolved the ghosting effect (false object shadow) in the generated frames. As a result, our model achieved a superior image animation generation effect compared to previous methods.