Kati8/ICLR_2023_2025 huggingface dataset:
authors: Jingzhi Bao
authors: Xueting Li
authors: Ming-Hsuan Yang
reviews: 
Comments: This paper introduces 4D scene texturing to generate textures that are consistent both temporally and across multiple views for animated mesh sequences. Tex4D, uses 3D geometry to synchronize diffusion processes, incorporates video generation model insights for temporal consistency, and modifies the DDIM sampling process to improve texture clarity.
Strengths: The paper is the first work to perform video generation based on animated mesh sequences, while its UV mapping strategy ensures multi-view consistency. The experimental results show significant advantages compared to some existing works.
Weaknesses: Under the current pipeline, this work has yielded highly effective results. However, the importance of this pipeline should be further clarified, such as by comparing it with pipelines based on 2D poses or textured meshes. The paper should include more comprehensive comparisons to highlight the contribution of the pipeline. For example, is it a reasonable pipeline to first generate textured meshes and then use animated meshes for video generation?
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2
reviews: 
Comments: This paper proposed a 4D scene texturing approach by video diffusion models, a zero-shot pipeline for generating temporally and multi-view consistent textures. In order to aggregate multiview latents in UV space, they discovered the issue of "variance shift" caused by their aggregation, and proposed to modify DDIM sampling process to address the issue. By UV blending during denoising steps, the issue of self-occlusion is addressed and synchronized in invisible regions.
Strengths: 1. The authors assert that this is the first method developed specifically for 4D scene texturing.
2. The authors introduce a multi-frame consistent texture generation technique, demonstrating improved consistency in results compared to baseline methods.
3. The paper is fluent and well-written, contributing to its readability and overall clarity.
Weaknesses: 1. The generated textures do not blend seamlessly with the background, creating a disjointed appearance that resembles separate foreground and background elements stitched together.
2. Despite claims of multi-view consistency, flickering effects are observed across different views, indicating instability in rendering.
3. Some of the compared methods, such as TokenFlow and Text2Video-Zero, do not utilize mesh or depth inputs, making direct comparisons less equitable.
Recommendation: 5
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 3
reviews: 
Comments: This paper introduces a novel framework for generating textures for mesh sequences. The authors utilize a depth-conditioned video diffusion model to ensure temporal consistency in videos generated from rendered mesh sequences for each predefined viewpoint. To achieve multi-view consistency, they adopt a UV space texture aggregation strategy. Additionally, they propose a modified sampling approach to address the issue of blurriness in the generated textures.
Strengths: See below.
Weaknesses: See below.
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 2