1. The Sec. 3.1 for 3D Gaussians generation seems to just follow the previous work, Luciddreamer. Please correct me there is any additional novel effort for this part.
2. Reprojecting the point cloud from the single view image to different views always leads to the holes, distortion and some other artifacts. A common idea to incorporate the generative model to fulfill the missing information. But it seems the author does not include anything related to this point. Please include more discussion if it is not necessary.
3. Minimizing the reprojection error for the predicted motions seems to be feasible in Sec. 3.2, However, this optimization is still based on the 2D information and can hardly achieve the 3D consistency. For example, in Structure from Motions, such method is always used refine the estimated camera poses and the position for the point clouds. But it does not consider the inter relationship between each 3D positions, so I am not sure that the 3D consistency can be achieved with this method.
4. Please provide more implementation details of the 3D Motion Optimization Module. It seems to be vague now.
5. The Sec. 3.3 for 4D Gaussians generation seems to just follow the previous work. Please correct me there is any additional novel effort for this part.
6. The experimental results will be more solid and comprehensive if more dataset and baselines can be included. For example, the author mentions that simply using animation based method cannot achieve satisfying results, so similar baselines can be included.
7. The paper presentation can be more compact if the part of just following the previous work can be shortened while emphasizing the novel part.
1. The Sec. 3.1 for 3D Gaussians generation seems to just follow the previous work, Luciddreamer. Please correct me there is any additional novel effort for this part.