reviews: 
Comments: The paper presents TANGO, a novel framework for generating co-speech body-gesture videos that effectively synchronizes gestures with speech audio. The key contributions of TANGO include:

Motion Graph-Based Retrieval: TANGO enhanced the motion graph-based model with more fine-grained designed AuMoCLIP to obtain relevant body gestures that align with the audio input, enhancing the realism and relevance of the generated gestures.

Diffusion-Based Interpolation Network: The framework utilizes a diffusion-based approach for video frame interpolation, which improves the quality and fluidity of the generated videos, addressing limitations found in previous methods.

The authors conduct extensive evaluations, including both objective metrics and user studies, demonstrating that TANGO outperforms existing methods like Gesture Video Reenactment (GVR) and Speech-Driven Templates (SDT) in terms of video quality, audio-motion alignment, and overall user preference.
Strengths: For the method design:

1. This work presents a retrieval framework for gesture generation. It resolved a significant issue by the former work GVR (retrieval naively relies on audio onset features and keyword matching) 

2. The author represent the gesture patterns within the video as graph and present an efficient pruning strategy to achieve long-sequence representation. This strategy seems effective.

3. The author propose the AuMoCLIP for aligning the two modalities and demonstrate an effective merging of text semantics, high-level audio, and low-level audio features. The ablation study for low-level and high-level retrieval is insightful with very detailed analysis and design. The author analyzed how the temporal alignment of two modalities can be achieved and also the high level semantics.

4. While uplifting the geature pattern generation to the pixel-level video synthesis, the author did not simply reply on commonly used ReferenceNet-like modules but in addition propose homography background offset flow for the consideration of camera changes for realistic videos. The author did very deep analysis of temporal dependencies and resolved the background and jittering problems introduced by camera motion commonly seen in video generations.


For potential insight and benefits for Future studies:
1. Auto-Evaluation: The AuMoCLIP seems to be a very good generalizable evaluation method for automatic evaluation of gesture motion naturalness conditioned on speech. Unlike FGD, diversity or Beat Alignment (BAS), which cannot precisely include the naturalness of motion patterns, the semantics or beat alignment of audio and speech, this model might be very beneficial for future auto-evaluations, though the author did not propose this point.

2. Audio and Gesture Motion Modality alignment: Previous works in this domain have very limited analysis of the how the speech signal functions as the trigger for gesture patterns. Many previous works, (like DiffSheg, CaMN, TalkShow, EMAGE, AMUSE, etc) only designed the generator by incorporating the audio features or text as the control signals to drive the gesture generation but never analyzed how the inner relationships of different modality should be. GestureDiffuCLIP did preliminary studies in aligning the speech word context and gesture motion patterns but ignored the temporal alignment between modalities. In this work, the author did very detailed analysis of the low-level retrieval and high-level retrieval, reconsider the temporal alignment and semantic alignment (low-level indicates the temporal alignment, I consider the local temporal contrastive loss might functions for the beat-dynamic of the gestures, high-level indicates the global semantic alignment of the two modalities. The ablation of the retrieval is very insightful)
Weaknesses: 1. The dataset for comparison are limited. The author only selected Oliver in the Show dataset and a small scale in-the-wild YouTube videos. (However, as far as I know, this filed lacks of high-quality data, PATS is another option, but it contains too many low-quality/low-resolution videos and blurry frames for large shoulder and hand motions)

2. It seems like the temporal contrastive learning is based on the low-level features obtained from the CNN encoder. I am not quite sure if I understand it correctly. It will be better if the author can include this information in the final draft for easy understanding.

3. For the method design, in case I am uncertain of some technical details, I will present some potential weakness in the Question section instead of here.

4. For the gesture motion representation, the author utilized 3D SMPL/SMPL-X template model parameters. I assume the author based on the first frame of the given image, do the tracking to obtain the camera and based on the camera to project the generated 3D joints onto the 2D-space for image-level pose guided generation. I think the author could add this information to the final draft.

5. Some of the training details of the model is missing, like what are the GPUs for training, time for training, learning rate.
Recommendation: 8
Reviewer confidence: 5
Soundness/correctness: 4
Clarity: 4