reviews: 
Comments: DreamGaussian is a novel 3D content generation framework designed to efficiently produce high-quality 3D content. The core innovation is a generative 3D Gaussian Splatting model paired with mesh extraction and texture refinement processes in UV space. Unlike the occupancy pruning seen in Neural Radiance Fields, this approach uses progressive densification of 3D Gaussians, which results in faster convergence for 3D generation tasks. The framework also incorporates an algorithm to transform 3D Gaussians into textured meshes and employs a fine-tuning stage for detail refinement. In tests, DreamGaussian was able to produce high-quality textured meshes from a single-view image in just 2 minutes, marking a tenfold speed improvement over existing methods.
Strengths: The paper's main contribution is changing the NeRF representation to Gaussian Splatting representation in the current text-to-3D or image-to-3D pipeline. This has significant challenges in terms of implementation. Also, the authors' current implementation allows for fast generation of assets, which has significant importance in the 3D field.
Weaknesses: Regarding the note “Similar to Dreamtime (Huang et al., 2023), we decrease the timestep t linearly”: Did the decrease of t help with the training time as well? Did it bring instability in training to the model? My previous exploration in this direction (for text to 3D in NeRF) showed improvement in the speed of generation for some objects but brought instability in the training. Since some objects needed more training steps than others, having a fixed annealing strategy damaged the performance, specifically for objects (e.g., motorcycles or dogs). It would be great if the authors could explore, in text to 3D, what the effect of the speed of annealing would be for different sets of prompts, (e.g., a corgi vs. a tree). Does the speed of annealing need to be tuned for each prompt?


The authors provided the experiment for time annealing in Fig 7 for image to 3D, but the paper is missing the same figure for text to 3D. Also, the effect of the speed of annealing needs to be explored.

Regarding the loss function, eq. 6, for UV-Space texture refinement: what happens if the object boundary in the Img2Img stage changes? How do the authors prevent the color of the background from leaking into the mesh color?

It would be great if the authors could compare the proposed texture refinement to other SOTA methods like TexFusion [1] or Text2Tex [2] or any other method of their choice. The reason for this ask is because texture refinement has been applied on top of the mesh. So, if the authors want to consider texture refinement as one of their contributions, they should either compare it with other baselines or reframe the paper and consider this as a side contribution.


I also tried experimenting with the code (great codebase!) and observed that, for text to 3D, the texture in most cases was very saturated. It would be great if the authors could comment on this phenomenon in the paper as a shortcoming and provide some insight into which parameter tuning might help.


Regarding the Janus problem, the authors provided a list of papers that address the Janus problem mostly using 3D data. However, it would be great if the authors could also reference methods like Perp-Neg [3] or Prompt-Debiasing [4] that address the Janus problem without the need for 3D assets. This has significant importance because they don't introduce bias from 3D data into the pipeline. For instance, they allow for the generation of a dog without the strict square position for standing that comes from the 3D asset.
It would be great if the authors could also comment on the guidance of the SDS loss and the effect of increasing those values.

There are many typos in the paper, and it would be great if the authors could fix them. Examples are: on page 2, "Gaussian splitting" appears to be a typo; it should likely be "Gaussian splatting". “severl methods” on page 3 and “Dissusion” on page 5 is it supposed to be discussion?

It would also be great if the authors could comment on how to get the model to consider both the input image and the text prompt simultaneously in both the initial stage and the later mesh/texture optimization stage. At the moment, it seems to ignore the optional text prompt. Was this intentional?

The paper contrary to DreamFusion does not learn an MLP for the background, it would be great if authors could comment on why they made that choice and what are their findings. 

[1] https://openaccess.thecvf.com//content/ICCV2023/papers/Cao_TexFusion_Synthesizing_3D_Textures_with_Text-Guided_Image_Diffusion_Models_ICCV_2023_paper.pdf

[2]https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_Text2Tex_Text-driven_Texture_Synthesis_via_Diffusion_Models_ICCV_2023_paper.pdf

[3] https://arxiv.org/abs/2304.04968

[4] https://arxiv.org/abs/2303.15413
Recommendation: 8: accept, good paper
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 2 fair