reviews: 
Comments: The paper presents a novel dataset and method to tackle the problem of concurrent co-speech gesture generation for two persons in conversation. The dataset consists of about 70 hours of curated video footage consisting of various pairs of conversing people --- taken mostly from talk show videos in the public domain --- such that both their gestures are fully visible. The proposed method trains a diffusion network with a temporal interaction module (TIM) performing cross-attentions between the audio and motion features of the two persons. The output of the TIM serves as the conditioning signal for the denoising motion decoder to generate the 3D motions of the two persons from noise. The authors show the benefits of their proposed approach through quantitative and qualitative comparisons, ablation experiments, and a user study.
Strengths: 1. The authors have tackled the challenging problem of generating concurrent, two-person co-speech gestures, which is a natural expansion of the scope of current co-speech gesture generation methods, and meaningfully takes the field of human motion understanding forward.

2. The proposed dataset is carefully curated and thoughtfully designed to contain a sufficient quantity of two-person gestures, making it a good candidate for a benchmark for two-person interactive motion generation problems.

3. The proposed method is technically sound and provides a baseline on the proposed dataset.
Weaknesses: 1. Some additional dataset preprocessing details may be useful for completeness.

    (a) Do the authors manually check the three preprocessing steps for each video (Lines 794-801)? How are quality and consistency ensured across the dataset?

    (b) How are the occlusions determined in the videos? Also, if any person in the video has a non-sitting posture, e.g., standing or walking around, are those motions tracked and filtered out?

2. Some details and motivations of the proposed approach are missing.

    (a) Why do the authors use $C_{mix}$, and not a combination of the cleanly separated signals $C_a$ and $C_b$, to get the interactive motion embeddings (Line 266)? It would also be good to see any mathematical relationships, visual representations, or ablation experiments to understand how $C_a$, $C_b$, and $C_{mix}$ relate to each other (e.g., is $C_{mix} = C_a + C_b$)?

    (b) During the generation, do the authors consider any global translation and orientation? In other words, do they consider any translation and orientation for the root joint, or is the root fixed in place? The visual results seem to suggest the latter, but it is not clear from the paper.

    (c) Since the authors only consider upper body motions (Line 244), why are they using a foot contact loss (Eqn. 4)? Is it possible to quantify the benefits of the foot contact loss for this problem (e.g., through an ablation experiment)?

3. The baseline methods the authors compare with (Sec. 4.2) are designed for single-person co-speech gestures. How do the authors adapt them for two-person co-speech gesture generation? Also, why are the authors not performing quantitative comparisons with more relevant two-person motion generation methods, such as InterGen (Liang et al. 2024b) or InterX (Xu et al. 2024)?

4. Some user study details are also missing.

    (a) How many generated videos does each participant watch?

    (b) What is the mean and variance of the lengths of the videos they watch?

    (c) What fractions of those video lengths contain the motions of person A, and what fraction contains the motions of person B? This might be calculated by assuming a person has motion in a particular frame if the minimum difference in their joint positions from a few previous frames is above some empirically determined thresholds.

    (d) What is the standard deviation in the participant responses? Particularly, it seems that the mean values for InterX, InterGen, and the proposed method are quite close.

5. Most of the visual results only show gestures of one person while the other person is sitting idle, and most of these results are only 3-4 seconds long. It is hard to appreciate the two-person motion generation performance from these results. The one example which is longer than 10 seconds and shows the gestures of two persons also exhibits jittery motion. Have the authors investigated the source of this jitter and explored any steps to reduce or remove it? Also, it would help to see some quantification of the balance of motions between the two persons (e.g., highlighting a person when they are gesticulating, showing the number of frames the person is gesticulating for) in the generated results.
Recommendation: 8
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3