# Object-Wise Video Editing
## 4 Experimental Result
### Novel View Video Synthesis  
We evaluate our Novel View video Synthesis on our own video clips that mostly have a close object to obtain a large movement of an object along with the change of View. Figure 17 shows the results of our Novel view video synthesis where \(F_{1}\), \(F_{2}\), \(F_{4}\) and \(F_{5}\) are the few frames of input video. We create a virtual camera near the path of the input frame giving a translation along the direction of perpendicular to the camera movement. In Figure 17, \(V_{1}\), \(V_{2}\), \(V_{3}\) and \(V_{4}\) are the virtual camera created from near input frame \(F_{3}\) having only translation along y-direction. \(V_{1}\) and \(V_{2}\) are translated in positive direction where \(V_{4}\) and \(V_{5}\) are translated in negative direction. The result of virtual view \(V_{1}\), \(V_{2}\) and \(V_{4}\) of our method shows that closer object movement is bigger than the farther object rather than the shifting the image along with the change of camera movement. \(V_{5}\), \(V_{6}\), \(V_{7}\), and \(V_{8}\) are the virtual view cameras that are created with translation and rotation from \(F_{3}\). The novel view result of \(V_{5}\), \(V_{6}\) and \(V_{8}\) are shown in Figure 17.  
In our NVS experiment, we create virtual views \(V_{9}\) and \(V_{10}\) giving translation along z-axis toward the scene which is demonstrated in Figure 18. Here, virtual view \(V_{10}\) is created in front of the foreground object and \(V_{9}\) is created behind the object. The novel view result for \(V_{10}\) shows that the background near the foreground object is occluded because of the view angle of the camera. This ensures that our method can produce a close view of a scene rather than zoom-in the original view. The result of \(V_{9}\) is created behind the foreground object from where the only background of the scene can be observed. In this case, our NDD scores successfully collect background and avoid foreground points because the ray segments are positioned behind the foreground object. It is very difficult to provide an objective evaluation of our novel view video synthesis method because we do not have the ground truth of novel view. Thus, to provide an objective evaluation, we consider any view of input video as novel view and inpaint the novel view without using that particular target view in our inpainting process. To calculate Peak signal-to-noise ratio (PSNR) and Structural Similarity Index Measure (SSIM), we collect 50 inpainted views and their respective original views from two video clips. Our inpainting method got an average PSNR 21.82 and SSIM 0.71. As our result have unpainted region in the border. So we exclude 15 pixel from the border in our PSNR and SSIM calculation. A comparison between inpainted view and original view are shown in Figure 19.  
Our NVVS depends on the extracted depth and the depth is created using photo-consistency over the epipolar lines of neighboring frame which lie on the direction of camera movement. Even though our method of depth extraction is not robust to the uniform color area, our NVVS method can render better results near the path of the input video. If the wrong depth is calculated in one reference frame, our refining method can ignore that point considering the color redundancy of the collected set of potential points. In the case of rendering a novel view video having a bigger distance from the path of camera movement, our method highly depends on the depth accuracy. These results  
Figure 16: Several failure cases in challenging conditions. show that our proposed inpainting method using NDD can be generalized for both object removal and Novel view video synthesis. Figure 17: Novel view synthesized video result where first column (**a**) is the input video sequence, second column (**b**) is the synthesized result of translated view along y direction, third column (**c**) is the synthesized result of translated view along y direction with rotation. Figure 19: Comparison between original view and synthesized view of our method.