reviews: 
Comments: The paper addresses the problem of 3D content generation from text- or single view inputs into 3D gaussian splats which can further be transformed into textured meshes. The proposed method is coined DreamGaussian and revisits the recent ToG 2023 3D Gaussian Splatting paradigm with a generative twist to it.

The proposed contribution is favorably compared to a comprehensive set of state-of-the-art comparative baselines and in particular is able to produced high fidelity mehses of objects within 2min of compute time, which is remarkable.
Strengths: + ## Readability.
As it currently stands, the paper is very well written. The main ideas and concepts are mostly well explained and articulated throuthout.

+ ## Organization of the contents and overall paper structure.
The contents are also very well structured and balanced.

+ ## Overall maturity of the submitted package, which makes it very reasonably within camera ready territory.

+ ## The actual performance of the proposed methodological contribution.
In particular, it dramatically cuts down the computation time in the space of optimization-based techniques in the field, by an order of magnitude.

+ ## Related work section and discussion. 
It is very well structued, articulated and populated with very relevant and up to date references.
Weaknesses: +  ## 1. Missing bits of context information - How much does it cost?
While indicative timings and implementation details (covering experimental setup,  are provided, information regarding the resource usage, model size and complexity are currently underdescribed.

A comparative disclosure of such information covering the main experimental baselines that are considered would help the reader better assess its relative positioning throughout the typical criteria.

Mentioning where the computation bottlenecks lie in terms of pipeline components would also be valuable in order to fully assess the practical usefullness of the proposed sequential pipeline, beyond rough timings.

+  ## 2. Challenging the ad hoc meshing post processing.

As it currently stands, the  mesh extraction technique relies on many subsequent post-processing steps, including mesh decimation and remeshing (end of page 5 in the main paper). I believe the explainations around eq. (4) (before and after) could be further improved and detailed. My current intuition is that the mesh complexity at least could be controled jointly during the density query step. Also, given the lack of statistics given regarding each step (Weakness 1 above), the relative need and ROI to fuse these steps is also hard to assess.

The current procedure also produces non-manifold and non-watertight meshes with arbitrary complexity.

+  ## 3. Evaluation.
The user study (ie, subjective quality analysis) that is presented in the main paper and further detailed in the appendix is a good idea and often overlooked in the field. 

Nevertheless, its size and statistical informative validity are rather limited as they are based on a "cohort" of 40 users and 15 input samples to assess from.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 4 excellent