reviews: 
Comments: This paper introduces a NVS method that leverages large pre-trained video diffusion models without additional training. The approach adaptively modulates the diffusion sampling process using input views to produce high-quality results from single or multiple views of static scenes or dynamic videos. Theoretical modeling is used to iteratively adjust the score function based on scene priors, enhancing control over the diffusion process. The modulation adapts to view pose and diffusion steps.
Strengths: 1. The proposed approach is entirely training-free, meaning it directly leverages pre-trained large video diffusion models without requiring additional fine-tuning or retraining. This feature not only reduces computational demands but also makes it adaptable to a wide range of applications where time or resources for training may be limited. The flexibility of using pre-trained models enhances its practicality, allowing users to apply this method to various scenes and tasks with minimal setup.

2. The generated videos maintain high visual fidelity, delivering smooth results. This quality stems from the adaptive modulation of the diffusion process, which effectively incorporates scene details and structures from the given views, ensuring that outputs are kind of realistic.

3. The method is underpinned by theoretical modeling, which guides its adaptive modulation strategy. By iteratively adjusting the score function with scene priors and analyzing estimation error boundaries, the approach achieves both controlled and adaptive modulation of the diffusion process.
Weaknesses: 1. The comparison between this method and NeRF-based methods is fundamentally imbalanced. NeRF techniques incorporate an underlying 3D structure, enabling them to render any view with predictable performance, as the 3D structure informs which views are feasible and which are not. In contrast, the proposed method lacks an explicit 3D representation, limiting its view synthesis capabilities to specific views with no guarantee of consistent performance. This distinction is significant, as NeRF's inherent 3D information allows interpretable, reliable results across views, whereas this method’s output reliability is less predictable and may vary based on input views.

2. To achieve a fair comparison between the proposed method and NeRF-based techniques, the authors should first reconstruct a 3D model from the output video of this method and then re-render the scene from that reconstructed model. This process would allow for a direct assessment of both methods’ rendering consistency and quality, ensuring that comparisons consider the 3D structure NeRF inherently leverages. Also, reconstruction error like PSNR should be reported.

3. The video results of the proposed method exhibit visible flickering artifacts, which could substantially affect reconstruction quality and consistency. A deeper analysis is needed to assess how these artifacts impact overall reconstruction accuracy and to identify potential mitigation strategies. This might include tuning reconstruction parameters to minimize flickering, which would help improve the method’s output stability and robustness, especially for applications sensitive to temporal consistency.

4. A major contribution is the derivation of the parameter $\lambda$ in Section 4.2, which aims to minimize the estimation error upper bound in Equation 15. However, a gap remains between this upper bound and the actual estimation error represented in the left side of Equation 15. To strengthen the theoretical foundation, the authors should provide a more comprehensive analysis of how reducing the upper bound affects the actual estimation error. This could be achieved through statistical analysis and empirical evidence showing how well the method reduces estimation error in practice, thereby validating the theoretical assumptions.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3