# Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability
## 4 Conclusions and Future Work  
In this paper, an improved framework for image animation generation has been proposed based on the FOMM method. Specifically, the two novel modules have been proposed and applied to solve the problems of inaccurate reconstruction and low quality of visual effects in video frame generation.  
Firstly, we proposed to use an attention module to optimize the generation of the optical flow field. The attention module can further enhance feature expression by reconstructing the feature information on the channel and space, so as to predict the more precise  
Figure 14: The reconstruction failure case of our method. (**a**) An error caused by the character’s clothing color was very similar to the background color. (**b**) An error caused by the source frame was lacking some information.  
Figure 13: Our method and FOMM’s visualization of keypoints in the TaiChiHD dataset. (The different colorful dots represent different key points, and the red circles indicate the incorrect key points.)optical flow field. Secondly, we proposed the multi-scale occlusion restoration module to obtain an occlusion map, with resolutions of (64, 64), (128, 128) and (256, 256), to repair the feature representation of the network at different resolutions and to enhance the repair ability of the network. With this proposed module, the generated frames can contain the correct and complete visual information and be of better visual quality in the case of large posture changes of the animated object. In addition, our model can be trained effectively in an unsupervised manner. Based on the above two modules, we proposed our improved framework. In order to verify the performance of our method, we conducted extensive experiments on three benchmark datasets, TaiChiHD, VoxCeleb1 and MGif. The experimental results showed that our method outperformed the FOMM in both the reconstruction quality and the animation quality.  
Although our proposed framework has achieved apparent improvement for image animation, there are still some limitations. As one of the limitations, the inter-frame correlation was not considered. In future work, we plan to utilize neural networks, such as LSTM [25], to save the generation results of the previous frame, and then the saved information can be used enhance the generation of the current frame. Given the correlation between the two consecutive frames, some generated content from the previous frame will help to improve the reconstruction quality of the current frame. Additionally, we also plan to explore the use of multiple source frame images from various angles to build potential source frames. We will automatically adjust the contribution of source frames from different angles through neural networks to generate more accurate and realistic reconstructed frames in our future work.  
Conceptualization, F.X.; methodology, Y.X., J.C. and F.X.; software Y.X.; validation, F.X.; formal analysis, Y.X.; investigation, J.C. and F.X.; resources, F.X.; data curation, Y.X; writing--original draft preparation, Y.X.; writing--review and editing, F.X.; supervision, Q.L.; project administration, F.X.; funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the fund of the Beijing Municipal Education Commission, China, under grant number 22019821001.  
Not applicable.  
The data presented in this study are openly available in [[https://github.com/[PERSON]/first-order-model](https://github.com/[PERSON]/first-order-model)]([https://github.com/[PERSON]/first-order-model](https://github.com/[PERSON]/first-order-model)), accessed on 9 December 2021.  
The authors declare no conflict of interest.