reviews: 
Comments: This paper introduces a novel approach that suggests generating multiple views (16 views in the model) of images simultaneously using a diffusion process. The model is built upon the foundation of zero123. It puts forward a volume-based attention mechanism to facilitate communication among the various views. In terms of performance, it stands out with exceptional results, particularly excelling in multiview alignment. Remarkably, this paper marks a significant milestone as the first diffusion based approach (to the best of my knowledge) among all single-view 3D generation models that doesn't require SDS for 3D model creation.
Strengths: 1. The paper addresses a crucial problem in the field by tackling the issue of multiview inconsistency in generated images. This is a significant challenge as inconsistent images often require additional techniques like SDS to derive 3D information. SyncDreamer's ability to generate multiview-consistent images directly addresses this problem, reducing the need for such supplementary methods.

2. The proposed volume-based approach in SyncDreamer is a novel and innovative design. It effectively incorporates geometric regularization into the model, which enhances the generation of multiview-consistent images. The design maintains an elegant and straightforward implementation, making it a notable strength of the paper.

3. The performance of SyncDreamer is highly impressive. It stands out as the first paper in the field to enable image-to-3D conversion without relying on techniques like SDS. This achievement is attributed to the model's ability to generate images with strong multiview consistency. SyncDreamer's capacity to produce high-quality and consistent results represents a significant advancement in 3D generation tasks, making it a remarkable contribution to the field.
Weaknesses: 1. One notable weakness of SyncDreamer is its computational cost, particularly the significant memory requirements associated with the volume-based approach. This could pose scalability challenges when aiming for high-resolution image generation, which is an important consideration for practical applications.

2. A noticeable gap in the level of detail between images generated by Zero123 and SyncDreamer raises questions about the specifics of this discrepancy. Given that SyncDreamer leverages Zero123 as a pretrained model and maintains it frozen during training, it would be valuable to investigate whether this difference is influenced by the volume-based attention mechanism or other factors, providing a deeper understanding of the model's limitations.

3. The fact that SyncDreamer is fine-tuned on Zero123 raises concerns about its generalizability and performance when trained from scratch, especially considering that both models use the same datasets. If SyncDreamer heavily relies on a strong backbone like Zero123 to achieve its results, its broader applicability might be limited, and it would be beneficial to explore how well it performs without the pretraining.

4. The concatenation of features across all views in the volume, without a mechanism to handle occluded views, might lead to suboptimal performance, particularly in cases involving heavily occluded objects. A more refined approach to selectively attending to relevant views during convolutional operations could enhance the model's robustness.
Recommendation: 10: strong accept, should be highlighted at the conference
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: 4 excellent
Clarity: 4 excellent