reviews: 
Comments: The authors present a co-speech gesture generation approach through a gesture video reenactment framework. To align video segments with audio, the author proposes an audio-motion joint embedding space using contrastive learning.

Furthermore, an existing diffusion-based, pose-driven video generation model is employed to produce interpolated frames. By incorporating an additional reference motion module and a homography-based background flow, the method achieves improved appearance consistency.

Both qualitative and quantitative comparisons with existing state-of-the-art methods demonstrate that the proposed method outperforms alternatives.
Strengths: 1. Compared to existing works, the video results of the proposed method are strong in terms of both visual quality and audio-gesture alignment.
2.  The authors conduct thorough experiments to validate the effectiveness of the proposed method.
3. In terms of novelty, I would categorize this work as moderate. Although using contrastive learning for audio-motion retrieval is not new, the author deserves credit for applying it within the speech gesture retrieval domain. Beyond this, other contributions seem more incremental, such as adding graph pruning to GVR and incorporating an additional reference motion module and homography-based background flow to the animation process.
Weaknesses: 1. Some claims are not sufficiently justified. For example:

    The author claims, “BERT captures high-dimensional language semantics rather than just ‘audio textures,’ which is critical for the co-speech gesture retrieval task.” However, there is no analysis showing that the addition of BERT features actually generates language-semantics-aware gestures. It’s possible that the performance improvement is due to alignment information alone. If the author could conduct an experiment comparing the use of BERT features with using only CTC alignment information, this might demonstrate the necessity of BERT. Additionally, if the author could provide video examples showing that the generated gestures become more language-semantics-aware with the addition of BERT, it would further support the claim.

2. Since the authors compare their work to other co-speech gesture video generation methods, it would be beneficial to include recent state-of-the-art works, such as *Co-Speech Gesture Video Generation via Motion-Decoupled Diffusion Model (S2GMM).*”If the authors could add this to their evaluation, it would make the proposed work's claim to be state-of-the-art more convincing.

3. Both the objective and subjective evaluations use only one character, Oliver, making it difficult to verify whether the proposed approach would outperform other methods in different circumstances.The authors could perform a comparison experiment with an additional character to demonstrate the generalizability of their approach. Furthermore, if the proposed method does not perform well on characters commonly used by other state-of-the-art methods (e.g., TALKSHOW, PATS), it would be essential to discuss these potential limitations.

4. Writing and formatting improvements are needed for easier comprehension. For example:

- 4.1 Abbreviations:
    
    - GVR stands for Gesture Video Reenactment in the abstract, but GVR is later used to refer to the specific work (Zhou et al., 2022).
        
    - The abbreviation for (Liu et al., 2022c) should be ANGIE, not ANGLE.
        
- 4.2 Tables:
    
    - In Table 2, methods are listed as columns, while in Table 1, they are listed as rows.
        
    - There are no captions to explain some non-trivial numbers in the tables, which could be helpful for interpretation.
        

5. Some explanations and implementation details are unclear or missing. Please refer to the question section for further clarification.
Recommendation: 8
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 2