reviews: 
Comments: This paper proposes a framework for generating co-speech body-gesture videos, dubbed TANGO. TANGO integrates a specifically designed Gesture Video Reenactment (GVR) module, which facilitates the realistic portrayal of gestures, alongside a hierarchical joint embedding space known as AuMoCLIP, which enhances the quality of video generation. The authors conducted extensive experiments that demonstrate the method's superiority over existing approaches in terms of well-rounded metrics and visualization results.
Strengths: This paper proposes a framework for generating co-speech body-gesture videos, dubbed TANGO. TANGO integrates a specifically designed Gesture Video Reenactment (GVR) module and a hierarchical joint embedding space known as AuMoCLIP to facilitate high-quality video generation. The strengths of this work are summarized as follows:
1. The authors present a novel and robust technical contribution through the introduction of a graph network baseline that adaptively incorporates the GVR and AuMoCLIP modules.
2. The motivation for this research is clearly articulated, and the insights provided are compelling. It is important to highlight that a unified framework for co-speech gesture modeling from audio to video sequences holds significant relevance in this community.
3. The dataset introduced in this paper is designed to facilitate ongoing research in gesture generation, providing valuable resources for future studies.
4. The visualization results are well-executed, allowing reviewers to thoroughly evaluate the findings and methodologies presented.
Weaknesses: However, there are still some questions for me. I encourage the authors to give reasonable responses. The final rating would be raised if the solution can solve my concerns.
1. Some important related works might be missed, and I encourage the authors to discuss with them. For example, both of these two methods leverage the audio signal as input to generate human postures.
 
[1] Qi, X., Pan, J., Li, P., Yuan, R., Chi, X., Li, M., ... & Guo, Y. (2024). Weakly-Supervised Emotion Transition Learning for Diverse 3D Co-speech Gesture Generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10424-10434).

[2] Mughal, M. H., Dabral, R., Habibie, I., Donatelli, L., Habermann, M., & Theobalt, C. (2024). ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1388-1398).

2. In the experimental setting, it is very interesting to explore the frame interval with different settings rather than 4. Please note that this is not an ask for additional experiments in the rebuttal, and this does not impact the paper score. I encourage authors to conduct this discussion in the future.

3. In the user study, did the authors assess participant engagement, specifically regarding whether responses were submitted too quickly or too slowly, and whether any such responses should be discounted? For example, whether the authors considered using response time thresholds or including control questions to check for consistent responses. I encourage authors to add these discussions in the appendix.
Recommendation: 8
Reviewer confidence: 5
Soundness/correctness: 4
Clarity: 4