reviews: 
Comments: # Summary

This paper focuses on creating temporally consistent and realistic textures for mesh sequences. The input is an untextured mesh sequence and a text prompt. To achieve this, a method named Tex4D is proposed, which is a zero-shot approach that integrates geometry information from mesh sequences with video diffusion models, specifically the depth-conditioned CTRL-Adapter (Lin et al., 2024), to produce multi-view and temporally consistent 4D textures. The model synchronizes the diffusion process across different views through latent aggregation in the UV space. Additionally, a reference latent texture is introduced to strengthen the correlation between frames during the denoising process.
Strengths: # Strengths

- The paper is well-written, making it easy to understand and follow.
- The related works are sufficiently covered.
- Several experiments are conducted for demonstrating its effectiveness.
Weaknesses: Regarding the contribution and motivation, I still wonder about the specific meaning of "4D texturing" for the object and how this differs from a 3D model that is first textured and then animated using skinning techniques. Even for dynamic textures, one could also generate the dynamic texture for a 3D model and then animate the character through skinning. This approach seems useful **if** the mesh is also significantly dynamic, such as with topology changes. Further, when it comes to the video background, I noticed in the supplementary video that there are some dynamic effects from the proposed method, but they are not that significant. Could one first perform 3D texturing and then render it with a generative background video?

I think the following suggestions could be helpful in justifying the significance of the setting in this paper.
- Provide specific examples or use cases where 4D texturing offers advantages over traditional 3D texturing and animation.
- Clarify how the proposed method handles dynamic meshes with topology changes, if applicable.
- Compare the method with a pipeline of 3D texturing followed by rendering with a generative background video, highlighting any benefits of the presented "integrated" approach.

I also have concerns about the novelty of the paper. The entire pipeline can be seen as a depth-conditioned CTRL-Adapter for mesh sequence texturing with UV space aggregation, which feels like a straightforward composition of existing models. I would prefer to see a simple yet effective method of tackling a critical problem. However, as I am still uncertain about the meaning/significance of "4D texture," this makes me somewhat skeptical about the proposed pipeline.

I think the authors could provide some arguments for the novelty of the proposed method:
- Highlight the key technical innovations in the pipeline beyond the composition of existing models.
- Explain how the introduced method addresses specific challenges in 4D texturing that are not easily solved by existing methods.
- Provide a clearer definition and motivation for "4D texturing" to help readers understand its significance in the context of their work (similar to the previous questions).

I understand the difficulty in evaluating the results, but it would be helpful and necessary to conduct an evaluation of Appearance Quality, Spatio-temporal Consistency, and Consistency with Prompt via a user study - The quantitative evaluation is insufficient.

The following action can be taken:
- Conduct a user study evaluating the specific aspects, e.g., Appearance Quality, Spatio-temporal Consistency, and Consistency with Prompt, and compare the proposed method with previous models.

Limitations and Future Works should be included. 
- For instance, the authors may discuss the current limitations of their approach, such as some failure cases or more specifically, the types of meshes or textures it struggles with, etc.
- Potential future improvements or extensions to the method.
- Broader implications or applications of this work in related fields.


# Minor Comments

- "To resolve this issue, we analyze the underlying causes and propose a simple yet effective modification to the DDIM (Song et al., 2020) sampling process." In the introduction section, it would be beneficial to briefly explain how you achieved this.
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3