## 3 Results
### Comparison of Subjective Quality  
In the subjective quality comparison, two comparison methods were used for verification. The first method was to use C4D v.R19 software to establish a standard model, and then regenerate the viewpoint images of the model at different viewpoints. Finally, they were compared with the virtual viewpoint images generated by the method in this paper, as shown in Figure 7. Figure 7a are the standard model images established by C4D, and the position of the model was set as x = 0. Figure 7b,d are the viewpoint images of the model generated by C4D at x = \(-3\) and x = \(+3\), respectively, and Figure 7c,e are the virtual viewpoint images of the model estimated by the method in this paper at x = \(-3\) and x = \(+3\), respectively. As can be seen from the experimental comparison results in Figure 7, for single RGB images with given viewpoints, the virtual viewpoint images at x = \(-3\) and x = \(+3\) obtained by estimation were of high quality. It can be seen from the contents marked by red boxes in Figure 7b,c that the houses and clouds in the occluded areas had been well extended after a series of \"learning\", which also shows that the virtual viewpoint images generated by the model in this paper can make full use of background information and edge information to fill in the invisible occluded area so that the content of the virtual viewpoint image was closer to that of the true viewpoint image. The second method was to use 3D image sequences ballet and breakdancers provided by Microsoft Research Asia to conduct the experiment. The image sequences contained eight reference viewpoints, and the camera distributions are shown in Figure 8. In this paper, the viewpoint frame image at the position of Cam4 was selected as the input image, and the virtual viewpoint images at the position of Cam3 and Cam5 were estimated, respectively; then, the inpainting results of the algorithm in this paper were compared with the true images at corresponding viewpoints. In Figures 9 and 10, image sequences of ballet and breakdancers were used, respectively, for the experiment. Figure 9a is the input image, which is the 10 th frame taken by Cam4. Figure 9b,c are the 10 th frame images taken by Cam3 and Cam5, respectively. Figure 9d,e are the virtual view images of Cam3 and Cam5 generated by the method in this paper, respectively. Figure 10a is the input image, which is the 20 th frame taken by [PERSON]. Figure 10b,c are the 20 th frame images taken by [PERSON] and [PERSON], respectively. Figure 10d,e are the virtual view images of [PERSON] and [PERSON] generated by the method in this paper, respectively. It can be seen from Figure 9d that the mural on the wall occluded by the ballet  
Figure 8: Camera distributions. Figure 7: Virtual viewpoint images generated at different positions. (**a**) The images of the standard model established by C4D, and the model position is x = 0; (**b**) Viewpoint images of the model generated by C4D at x = \(-3\); (**c**) The virtual viewpoint images of the model estimated by the method in this paper at x = \(-3\); (**d**) Viewpoint images of the model generated by C4D at x = \(+3\); (**e**) Virtual viewpoint images of the model estimated by the method in this paper at x = \(+3\). dancer was filled with information according to the direction of the texture of the mural after \"learning\", and the filled content was very close to the true image. It can be seen from the enlarged images in Figure 9d,e that the experimental results of the method adopted in this paper could well retain and extend the detailed information of each object when filling the occluded area. In Figure 10, the 20 th frame image taken by Cam4 was selected as the input image. In Figure 10e, for the information of the wall occluded by the male dancer, contents in different areas were also filled according to the difference in color.