reviews: 
Comments: This paper focuses on the task of interactive motion generation in two-person dialogue scenarios. It first introduces a large-scale dataset for two-person interactive motion, which includes body and finger movements as well as facial expressions. An interactive motion generation model is proposed, along with a Temporal Interaction Module to ensure the temporal synchronization of gestures. 
Based on the proposed GES-Inter dataset, the authors benchmark the model against other state-of-the-art algorithms.
Strengths: This paper extracts 3D data from videos, collecting a large-scale dataset. Compared to laboratory-based data collection, capturing natural interactions yields more authentic interactive motions.

The proposed method employs Bilateral Cooperative Diffusion and the Temporal Interaction Module, which shown to be effective in experiments.

The dataset separates audio for the two speakers, recognizes the text corresponding to the audio, and invites participants to review the data, which is commendable.
Weaknesses: **Dataset Quality**
Capturing accurate two-person interactive motions from video is inherently challenging, which raises concerns about the dataset's quality. From the GES-Inter dataset examples shown in the videos [3], there are noticeable issues, such as hand self-penetration problems for the yellow character. Additionally, the dataset's frame rate is only 15 FPS, which is lower than that of mainstream motion datasets, limiting the compatibility of this dataset with other human motion datasets.

Based on the dataset and generated examples provided, all individuals share the same shape parameters, showing no variation in body shape, and their root positions remain fixed. As a foundational dataset project, it is crucial for the authors to clarify any pre-processing or post-processing steps applied to the data and the motivations behind these decisions.

To help the authors further demonstrate the quality of GES-Interdataset and improve its compatibility with other datasets, I offer the following suggestions: **(1)** Provide more comprehensive example videos showcasing the dataset.
**(2)** Consider offering solutions to improve the dataset's frame rate, such as FPS enhancement tools or post-processing scripts.
**(3)** Further explain any pre or post processing applied to the data and clarify why there is no observable variation in human shape or root position in the dataset and generated examples.

**Comparison**
The dataset may lacks a comparison with previous [2]. The authors should consider adding a table to quantitatively compare the GES-Inter dataset with [2], highlighting differences in aspects such as size, capture equipment, diversity, and quality metrics. This comparison would provide a clearer understanding of the dataset's advantages and limitations relative to previous work.

**Interaction Metric**
The correspondence between the interactive motions and the given speech in two-person interactions appears weak. It may be helpful for the authors to identify and categorize the typical types of interactive motions and consider developing or adopting more suitable metrics for evaluating the quality of interactions. In particular, the current metrics, such as FGD, BC, and Diversity, are insufficient for this purpose, and incorporating more interaction-specific evaluation criteria would strengthen the assessment..


**Audio process**
In the methodology section, audio input (C_mix) is simply fed into the Audio Encoder without special processing for each speaker’s audio, particularly in cases where overlapping speech occurs when both speakers talk simultaneously. The authors should clarify how their method handles overlapping speech. It may be helpful to conduct an ablation study or provide examples that demonstrate the model's performance specifically in scenarios with overlapping speech.

**Ambiguous Figure**
Figure 3 make ambiguous, the authors should revise the figure to accurately represent the inputs to the Audio Encoder as described in the text. Based on the paper's description, the input to the Audio Encoder should be C_mix={c_a,c_b}, rather than the isolated representation of C_mix,c_a and c_b  shown in the figure. To clarify, placing c_a and c_b  on the left side of the audio diagram and omitting the corresponding arrows to the Audio Encoder would be more accurate. 
Alternatively, if the figure is correct, you should clarify this in the text.

**Reference**

[1] Yi, Hongwei, et al. "Generating holistic 3d human motion from speech." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.

[2] Lee, G., Deng, Z., Ma, S., Shiratori, T., Srinivasa, S. S., & Sheikh, Y. (2019). Talking with hands 16.2 m: A large-scale dataset of synchronized body-finger motion and audio for conversational motion analysis and synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 763-772).
Recommendation: 6
Reviewer confidence: 5
Soundness/correctness: 3
Clarity: 2