Kati8/ICLR_2023_2025 huggingface dataset:
authors: Jiachen Li
authors: Qian Long
authors: Jian Zheng
authors: Xiaofeng Gao
authors: Robinson Piramuthu
authors: Wenhu Chen
authors: William Yang Wang
reviews: 
Comments: In the paper, the authors present T2V-Turbo-v2, a method to enhance diffusion-based text-to-video models by distilling a consistency model from a pre-trained T2V model. This approach integrates high-quality training data, feedback from multiple reward models, and motion guidance into the distillation process. Through ablation studies, it emphasizes the importance of high-quality datasets and diverse reward models to improve visual quality and text-video alignment. The method also verifies the effectiveness of incorporating motion guidance to enhance video motion quality. T2V-Turbo-v2 achieves a state-of-the-art total score of 85.13 on VBench, outperforming advanced text-to-video models like Gen-3 and Kling.
Strengths: 1. The experiments are comprehensive and thorough, with detailed analysis.
2. The analysis of minimizing CD loss using entire datasets while restricting reward optimization to short-captioned datasets is interesting and meaningful, which may encourage future work.
3. This paper establishes a new SOTA total score on VBench, leveraging open-source models to outperform some advanced text-to-video models.
4. The paper is well-written and easy to understand.
Weaknesses: 1. The integration of MotionClone into T2V-Turbo is an interesting direction. However, while the contribution highlights the potential for diverse forms of energy functions, this study primarily utilizes the motion representation from the MotionClone work without substantial modifications to the energy function's format. The other enhancements are relatively minor, such as the reward model used, which only adds a CLIP compared to T2V-Turbo, and the removal of the EMA model. It might be beneficial to explore further variations to strengthen the contribution.
2. It would be beneficial to include a discussion on the performance differences between VCM and the proposed method across various datasets. For instance:
    - Could you explore why VCM achieves the best performance using only OV, while the proposed method does not attain similar results? It appears that different methods may lead to varying conclusions regarding dataset choices. Additionally, how would the results differ if T2V-Turbo (not v2) was used?
    - Considering OV and VG are both high-quality video datasets, it would be insightful to analyze why OV+WV exhibits poorer performance compared to VG+WV, which performs quite well.
3. In the section on data preprocessing, the approach involves using DDIM Inversion on all videos to obtain the necessary motion guidance for training, which is effective in reducing training time. Nevertheless, this approach does not significantly simplify the overall complexity. It would be better to explore improvements to the motion guidance strategy itself to enhance training efficiency.
4. It would be valuable to include theoretical or experimental results to analyze why the EMA model in consistency distillation is unnecessary.
5. It would be better to conduct a user study to further verify the performance from a human perspective.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 4
Clarity: 3
reviews: 
Comments: This paper present motionclone-based consistency distillation, using motion guidance to improve temporal and spatial coherence.
Strengths: 1. This method demonstrates strong performance, achieving state-of-the-art (SOTA) results on VBench. Visualized video outputs appear smooth and high-quality, reflecting its effective design.

2. The paper is clearly written, allowing reviewers to easily understand the authors' intent.

3. This method is simple and effective, making it generally more practical.
Weaknesses: This method is overly engineering-focused and lacks novelty, as the motion guidance and consistency distillation techniques involved are already established, making it appear less innovative.

Additionally, while it conducts extensive ablation experiments on motion guidance and reward models, this does not constitute a significant contribution of the paper. I am unclear about the paper's contributions; is it providing more interesting insights? It would be helpful if the authors could briefly summarize this in their response.

Regarding the contribution summary of the paper (L117), it seems to emphasize the advantages of existing work and the potential of extracting motion priors. And, I do not see any strong insights that stand out; motionclone has already demonstrated this fairly clearly. If the authors mean that motion priors are particularly useful during T2V training, they should provide more experiments. For example, training SVD and VideoCrafter2 shows that the insights presented in T2V-Turbo are quite limited.
Recommendation: 6
Reviewer confidence: 5
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This paper introduces T2V-Turbo-V2, aiming at improving the video quality and alignment with prompts by focusing the post-training phase. It distills a pre-trained T2V model using various supervision signals, including, 1. the reward models feedback from pre-trained vision language models (CLIP and InternV2) for both image and video levels. 2. the self-consistency loss used in many other distillation models, and injecting the classifier-free guidance and energy function into the self-consistency loss. 
This paper also optimize the data preprocessing and the reward feedback, allowing the T2V-Turbo-V2 to achieve the state-of-the-art results on the Bench and outperform previous models like VideoCrafter2, Gen-3, and Kling.
Strengths: 1. The model achieves high scores across multiple metrics and outperforms proprietary systems, demonstrating the effectiveness of proposed modules.
2. T2V-Turbo-V2 introduce an effective post-training process to enhance video quality and prompt alignment, and this process if architecture agnostic, potentially can be used on other pre-trained video generation models.
3. This paper provide detailed ablation studies on various factors like the dataset selection, reward model configurations, the effectiveness of motion guidance, and so on.
Weaknesses: 1. The model appears optimized for single-caption, short-context prompts, its ability to generate longer or more complex video context may be limited.
2. When generating the dataset for the motion guidance, it may require considerable computation resources.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 4
Clarity: 3