# Object-Wise Video Editing
## 4 Experimental Result  
To evaluate our video editing approach, we showed both object removal and novel view video synthesis results in our experiment. For object removal, We use two video data sets--public videos from Reference [10] and our own video clips. The public videos also contain the mask information of the desired object to be removed along with the  
Figure 12: NDD Thresholding to collect more potential points to reduce the effect of blurriness on Novel View Video Synthesis. (**a**) NDD Thresholding based on the 3D pixel size of estimated depth; (**b**) Comparison between 3D pixel size NDD thresholding and fixed NDD thresholding. mask of other moving objects present in the video. In the case of our own video clips, they mostly contain static objects with a bigger region and we extract the mask of the object to be removed using the method [25] described in Section 2.1.1. In our novel view video synthesis, we use our own video clips to create a novel view synthesized video. The videos from the public background inpainting dataset [10] mostly contain far background, dynamic object, and regular patterns that have a small amount of camera movement. This dataset also provides the mask videos of desired object to be removed. This dataset contain seven real-world sequences. The videos are captured with handheld Canon HV20 digital camcorder at 25 fps. Small camera movement is enough to observe missing background information of relatively small dynamic objects inside a video. However, in the case of the static object, we need a bigger camera movement to observe the missing background information. If we want to remove a bigger static object, we need more camera movement to secure the missing occluded background. To simulate such cases, our video clips are taken mostly with closer patterned background and static foreground object using a hand phone. This is a challenging situation because we have large movement in the background that makes further degradation in inpainting performance due to the large change of viewpoint angle. A comparison between [PERSON] et al. [14] and our object removal method on diverse challenging videos are shown in Figure 13. Our inpainting method gives more clear results on first, second and fourth results. However, [PERSON] et al. [14] produces blurred inpainting results due to the bigger holes which cause a larger movement of patterned background. A comparison between [PERSON] et al. [14] and our object removal method on diverse challenging videos are shown in Figure 13. Our inpainting method gives more clear results on first, second, and fourth results. However, [PERSON] et al. [14] produce blurred inpainting results due to the bigger holes which cause a larger movement of patterned background. To observe the background, if we need large camera movement then truncated epipolar lines in the far neighboring frames become bigger lines which cause more number of potential points. A frustum is a pyramid structure view of a 2D camera and a pixel frustum represent the frustum view of single pixel of 2D camera. [PERSON] et al. [14] collect all redundant points from their pixel frustum which creates a region in the neighboring frames. The region of the pixel frustum is bigger in size when the camera has larger movement. On the other hand, our method collects a very small number of highly qualified points from the truncated epipolar line based on our NDD score and produces clean and sharp inpainting results. In Figure 14, we show all experimental results of the proposed method on our video clips taken from a handphone device. V1 has a close object with a bigger camera movement and complex visual pattern on the background. V2 also has a large camera movement with complex background shape so that many self-occlusion of background can be observed at different viewpoints. V3 has a moving foreground object with camera movement and v4 has a foreground with complex shape and the object is comparatively bigger. In V5, a narrow fence is the foreground object to be removed.