Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The work presents a deep GAN model to predict the 3d motion of humans in a social interaction. It introduces a dynamic intent model that models the interaction between the representation of the individual motions.
strength_and_weaknesses: Introduction level discussion: 

“stochastic forecasting of human trajectories in crowds …”
Advanced a lot with modeling the social interactions and multiple works showed this. 
For example, Trajectron++… etc. Authors should re-visit this statement in the introduction.

The “multi-person 3D motion forecasting” task already exists. See “Multi-Person 3D Motion Prediction with Multi-Range Transformers” . Claiming it is “novel” because the word “stochastic” is not accurate. Motion forecasting/ prediction is a generic task that can be solved deterministically or stochastically. 

The concept of local and global dealing with motion forecasting already existed in prior works such as Trajectron++ and follow works… etc. 


Method discussion: 
The discrete intent is positive of this work. Though, is not sampling the same intent for all individuals without conditioning on the individual trajectory might lead to mode collapse? 

Results discussion: 
- Are the ADE/FDE metrics best of N (B-o-N)? 
- How many samples used in calculating the performance? Does the results vary with respect to the number of samples?
- When evaluating 3D person motion prediction we are expected  to use the 3D path error and 3D pose error to understand the accuracy of the joints. Please see “Long-term human motion prediction with scene context “ and later works for these metrics. 
- What is the divergence of the prediction between 1,2,3 seconds? 
- Ablation study is positive. 

clarity,_quality,_novelty_and_reproducibility: The work is clear except the latent coding part. 
Novelty is limited. See above discussion. 
summary_of_the_review: The work is good. It need to re-visit several claims in the introduction part. 
The method needs a better illustration. 
The evaluation misses a core metrics and clarification of how the comparison versus deterministic models was constructed.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: In this paper, authors propose a dual-level generative model, considering the individual movements and social interactions. The proposed method achieves good performance on CMU-Mocap, MuPoTS-3D, and SoMoF benchmarks.
strength_and_weaknesses: **Strengths**:
Overall, it is a good paper with strong motivation. The method is clearly introduced and the experiments are sufficient and convincing.


**Weaknesses**:  
1. In the proposed method, why are the latent codes of the social interactions defined as the same? What's the insight here? In my opinion, the interactions are also dynamically changing during the observations, which is also pointed in [1,2]. Some explanations should be included here. In addition, why consider social interactions on a global level? How about the local social interactions?

2. In Sec. 3.1, Local-Level Modeling, $N$ different intents $z_{i}$ are extracted to generate independent future movements. Does $N$ represent the total number of people in one sequence or one batch or the whole dataset? I presume it is the number of people in one sequence or batch, then $N$ must vary in different sequences or batches. How to deal with this situation? More details should be included here.


3. Missing related works, some latest multi-person forecasting trajectory/prediction [1, 2, 3]. Since this work focuses on multi-person motion prediction, I think part of the discussion in Sec. B of the Appendix should be summarized in Related Work Section to highlight the differences between the proposed method and existing ones.

[1] Evolvegraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning. NeurIPS2020  
[2] Tra2tra: Trajectory-to-Trajectory Prediction with A Global Social Spatial-Temporal Attentive Neural Network. ICRA 2021  
[3] GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction With Relational Reasoning. CVPR2022   
[4] Adaptive Trajectory Prediction via Transferable GNN. CVPR2022  
[5] Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion. CVPR2022 

clarity,_quality,_novelty_and_reproducibility: Details are in [Weaknesses].

Clarity:
The clarity is good.

Quality:
Clear-written and well-organized.

Novelty:
Motivation is strong and novelty is good as well.

Reproducibility:
The training procedure is clear, and authors also provide the codes.
summary_of_the_review: I list my concerns in [Weaknesses], I am happy to discuss and increase the rating if my concerns are addressed.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper presents a novel architecture to performe multi-person 3D pose forecasting. The importance of pose forecasting is well known, useful in quite complex situations like autonomous driving.
The model is quite simple but effective, taking into account an important variable of the human behaviour which is the context and the others in the environment.
The paper is integrated with an extensive experimental section.


strength_and_weaknesses: strenghts:
- The model is quite simple, it is based on a generative model which based on two steps: the first one aims to predict the single-person pose in the future, the second one aims to analize the social interactions among people in order to refine the single-person pose.
- the model is able to predict interactions between people and can learn for example how to react to collisions.
- the paper opens up to new tasks and problems, it can introduce a new branch of pose forecasting leading to new models capable of better understandings of human behaviours.
- details on implementation of the code are well described in supplementary material

weaknesses: 
- Metrics used in my opinion aren't easy to understand from a practical point of view. It is difficult to have an idea on how the model can perform in terms of mm of error.
I suggest to use metrics like MPJPE or MAE to better understand the quantitative performances of the model.
- improving models with social aspect of the human movement can be really effective for the model performances. I would like to see an ablative study on how this model can improve performances compared to standard  single person forecasting models.
My doubt about this system is that performances compared to sota single person forecasting models would be lower, on standard datasets that offer even multi person poses (CMU panoptic, 3DPW).
clarity,_quality,_novelty_and_reproducibility: 
Clarity: the paper is clear and reads well
Quality: results are of high quality, but could have been more convincing with multiperson datasets
Originality: other work deals with social modules, this is the first dealing with social aspects


summary_of_the_review: The model is quite simple but effective, taking into account an important variable of the human behaviour which is the context and the others in the environment. It could have been ok to show some experiments on single pose forecasting too
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper focuses on the problem of multi-person 3D motion forecasting. The proposed framework uses a dual-level transformer-based generative model, encoding the local (per person) and global (multi-person) motion histories to decode each individual future body motion. The framework has been comprehensively evaluated on different datasets such as CMU-Mocap, 3DPW and MuPoTS-3D and also a benchmark, SoMoF, and demonstrates its superior performance against SOTA frameworks.

strength_and_weaknesses: Strengths:

1- The idea and motivation are clear and the submission is well presented, 2- a comprehensive literature review with relevant literature and research topics is covered 3- extensive experiments on different dataset and one related benchmark is provided, 4- the method achieved SOTA results on these datasets 

Weakness:

1-considering most generative models are well studied in multi agent trajectory forecasting, the paper’s technical contribution is yet relatively limited compared to the literature (though the problem here is not identical and the overall framework is yet different)

 2- a better qualitative visualisation of results would be helpful for better understanding of the framework behaviour 
clarity,_quality,_novelty_and_reproducibility: The clarity and quality of the paper is good. And based on the information provided in the paper, I guess the framework is reproducible. The novelty is not substantial but acceptable considering good results. 
summary_of_the_review: Although the technical contribution is not substantial, I think the paper has a good quality in the terms of presentation, experiments and results. Considering these aspects, I support the paper to be accepted in this venue. However I have some minor comments

1-  on the presentation of qualitative results: it can barely understand anything from Figs 3-5. The Errors and the good results could be highlighted. The figures are also too small to distinguish anything meaningful. 

2- I am not sure if I found out how did the predicted samples from generative models are evaluated during inference? I think eventually one sample from a distribution should represent the final output and this should be decided independently from evaluating them against ground truth. Do you calculate the mode of the generated samples? If so how?

3-  while the model is compared with most recent conference papers published on SoMoF benchmark, it would be great if the authors can comment on other published frameworks (eg workshop ones) which use very simple and non-transformer based frameworks with superior results. For comparison purpose, It would best if the authors make their results on SoMoF leaderboard published .
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The submission studies the problem of stochastic human motion forecasting in a multi-human setting, which is a more challenging setup compared with most existing works. Overall, reviewers appreciated the clear motivation, the simple model, and the good results. There were concerns about the presentation, missing related work, and implementation details. The authors addressed the concerns nicely in the rebuttal. In the end, all four reviewers support acceptance. The AC agrees.
justification_for_why_not_higher_score: For a paper like this, the quality of the predicted motion is important. Based on the supp materials, the results are good, but the detailed motion of humans is not captured. 
justification_for_why_not_lower_score: This is a solid submission and all four reviewers support acceptance (score: 8).
Decision: Accept: notable-top-25%