Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper proposes a Face Interaction Graph Network (FIGNet) to extend GNN-based rigid object collisions simulators for interaction computation between mesh faces, rather than nodes.  The proposed FIGNet is 4x more accurate in simulating complex shape interactions and 8x more computationally efficient on sparse and rigid meshes than learned node- and particle-based methods. 
strength_and_weaknesses: Strength

1.	The paper is well written so that it is easy to follow and understand the motivation and the proposed method.

2.	The proposed FIGNet is novel and experimentally demonstrated to be effective.

Weakness

1.	The experimental scenarios are a little simple, it would be better to conduct experiments on more realistic data.

clarity,_quality,_novelty_and_reproducibility: The paper is good in terms of the clarity, quality, and novelty.
summary_of_the_review: Based on the strength mentioned above, my current decision is accept.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper introduces a novel face interaction mechanism for GNN-based rigid body simulators to solve the penetration issue existing when the contact point is far from nodes. The new framework significantly increases the accuracy of contact resolution and enables the simulation of complex shapes. The framework is even more expressive than analytical simulators. It can capture subtle dynamic behaviors shown in real-world data that cannot be interpret by some existing rigid-body simulators.
strength_and_weaknesses: **Strength:**

- The illustrations are helpful in understanding the idea, and the technical details are complete.

- This extension to MeshGraphNets is elegant.


**Weaknesses:**

- The following reference may be closely related to the topic:

*Lei Lan, et al. 2022. Affine Body Dynamics: Fast, Stable and Intersection-free Simulation*

The above simulator can achieve competitive or faster rigid-body dynamics than Bullet, Mujoco, and PhysX, and can guarantee non-penetration for arbitrarily complex shapes. I would like to see a wall-clock time cost comparison to Bullet, Mujoco, and PhysX as well.

- There are only two contact scenarios for discrete mesh: point-triangle contacts and edge-edge contacts, as shown in the following reference:

*Minchen Li, et al. 2020. Incremental Potential Contact: Intersection- and Inversion-free, Large-Deformation Dynamics.*

The experiments would be more thorough if the authors could specifically examine how these two contacts are resolved.

- Mathematically, contact is just a non-penetration constraint for simulation. I would like to see **quantitative** statistics on penetration counts or penetration depths when making comparisons.

clarity,_quality,_novelty_and_reproducibility: The presentation of the paper is clear and easy to follow. The idea of adding a message-passing mechanism between faces and the proposal of the virtual object node are novel to me. 
summary_of_the_review: The paper proposes an extension to MeshGraphNets, which significantly increases the contact resolution accuracy. However, quantitative statistics about the efficiency and accuracy of the proposed framework as a rigid body simulator are needed.
review_title: null

final_score: rating: 10: strong accept, should be highlighted at the conference
review_content: summary_of_the_paper: This paper proposes a “Face Interaction Graph Network” (FIGNet)" architecture for accurately and efficiently modeling collisions between faces of meshes. Previous SOTA methods based on Graph Neural Networks (GNNs) model only collisions between nodes of the meshes of different objects and hence cannot deal effectively and efficiently with objects with large faces without requiring significant sub-division of the mesh's surfaces. There are two key innovations that the authors introduce into GNNs to solve this problem. The first is to introduce a mechanism for message passing between the face and the nodes that comprise it and the second is to introduce and object-level virtual node for efficient passing of information between nodes without requiring too many message passing iterations. The authors compare their approach to SOTA learning-based graph and particle methods for this task on a simulated dataset and against an analytical solver on a real-world object pushing task. They show SOTA performance in terms of accuracy, computational efficiency and generalizability of the proposed method versus all existing approaches.
strength_and_weaknesses: Strengths:
Novelty: The work solves the previously unsolved problem of how to enable effective and efficient message passing between colliding mesh faces in a GNN. To do so they introduce a task-specific module of encoding "Face-face edge features" along with mechanism for effective message passing between these new features and the existing nodes and edges of the graph. The proposed design is conceptually correct and well-motivated. The authors also (potentially) introduce a virtual object node at the center of each object to pass messages between distant nodes more efficiently. This too makes sense.

Clarity: The paper is well-written and the material is presented in a well-organized fashion.

Significance: The method shows impressive performance improvements over the SOTA methods in terms of accuracy and computational efficiency and generalization, outperforming many previous learning-based and analytical baselines. The authors' results on effectively learning real-world dynamics besides the ones from simulation are exciting and can open the door to many new applications.

Weaknesses:
1. It is a bit unclear from the text and experiments if the virtual object node is the author's novel contribution or it comes from prior works on particle based approaches. It would be good if the authors could state this more clearly in the text.

2. The authors have clearly acknowledged several limitations of their presented technique in terms of not being able to deal with stochastic outputs, requiring state information as opposed to predicting from images alone, and in terms of learning with even more complex objects with many orders of magnitude more points. I am satisfied with with this.
clarity,_quality,_novelty_and_reproducibility: Clarity: Excellent! The paper ie very well-written and a delight to read. The supplementary section is extensive and thorough and the results provided are numerous and compelling.

Novelty: Overall the work is highly original both in solving a previously unsolved problem and in proposing a novel architecture to solve it.

Quality: The quality of the presentation, the technical soundness of the work, the thoroughness of the experiments are all excellent.

Reproducibility: While the authors have not provided code or promised to do so, sufficient details of the experiments are provided in the supplementary section to enable reproduction of the work.
summary_of_the_review: Overall this paper introduces a significant and broadly impactful novel contribution to the computer vision research community, which can potentially open the doors to new research ideas and applications. Hence, I recommend accepting this work.
review_title: null

final_score: rating: 10: strong accept, should be highlighted at the conference
review_content: summary_of_the_paper: The paper introduces an extension of MeshGraphNet by adding explicit modeling of rigid body faces. The authors show that this modification significantly improves simulation performances over a large number of tasks while reducing the computational burden.
strength_and_weaknesses: I very much appreciated this paper which introduces with great clarity a method which seems reasonable. The addition of face-level interactions in the structure of GNNs is a significant improvement over standard models, and seems to me somehow related to previous works on dual graphs, e.g. [1]. A discussion of the key differences with existing works on the subject could perhaps be added.

FIGNet seems relevant and efficient, and the design choices presented are reasonable. I only have two questions about it:

- The collision distance $d_c$ seems to be an important parameter. Did the authors measure the effect of this parameter on learning and inference? Is it a sensitive hyper-parameter? Can we imagine learning $d_c$ directly with the weights of the model?

- Sorting the nodes of a face according to the distance from the nearest point on the other face is said to be "crucial". Can the authors discuss more clearly the significance of this? Have ablation experiments been performed?

Regarding the experimental results, these are indeed "plausible". But this also seems to be the case for MeshGraphNet when the collision distance is increased. The comparison with the baselines would be easier if the results were aggregated in a table, which could be added in the appendix. The authors justify the advantage of FIGNet by evoking the exponential complexity of the baseline when $d_c$ increases. Nevertheless, FIGNet trades off the increase in the number of edges against an increase in the complexity of the model. Finally, it seems to me possible that the gain of FIGNet is quite limited in practice. Thus, a more in-depth analysis of the difference between these two models seems necessary. It would be particularly interesting to measure their inference time (and why not the number of FLOPs).

Finally, one of the weaknesses of the approach is the need to have access to a rich and detailed representation of the scene, in the form of a mesh. Have the authors considered solutions to train their models from weaker signals, or even directly from images?

In addition to the quality of the work offered, I note the use of certain tricks to keep the number of pages to the limit. In particular, the referencing of figures in the appendix in the main text is questionable, but I recognize that this is common practice. What concerns me more is the modification of the style of bibliography compared to the standards of ICLR. It seems to me that this style is mandatory (while it requires a lot of space). If this is indeed the case, I would appreciate if the authors regularize their paper, for the sake of fairness with other submissions.

clarity,_quality,_novelty_and_reproducibility: The paper is very well written, the proposed model FIGNet is reasonable, interesting, novel, and seems to perform very well compared to existing baselines. The results seems reproducible, however code release would have been very much appreciated.
summary_of_the_review: The method presented in this paper is very interesting. I particularly appreciate the clarity of the writing as well as the extensive empirical study, and the care taken to offer solid and comparable baselines. My score is however reduced by a doubt as to the practical interest of FIGNet against MeshGraphNet in the case where $d_c$ is increased, and also particularly by the liberties taken by the authors regarding the editorial rules, which is not very fair with other submissions. If the authors provide satisfactory answers to these two points, I will gladly increase my score!

## EDIT
The authors answer my remark in a very complete and convincing way. I believe that the new experiments add great value to the paper and thus highly recommend this paper for acceptance.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: Four experts reviewed this paper with all accepted recommendations. The area chairs agree that this work makes a very important contribution by introducing a novel Face Interaction Graph Network (FIGNet)  to significantly improve the SOTA of learning rigid body dynamics. The reviewers did raise some valuable concerns that should be addressed in the final camera-ready version of the paper.



justification_for_why_not_higher_score: This paper would be much stronger to evaluate if this framework can be used for physical reasoning tasks [1,2,3]. Ac would urge the authors to include some discussions in the camera-ready version. 

[1] Physion: Evaluating Physical Prediction from Vision in Humans and Machines. NeurIPS 2021
[2] CLEVRER: CoLlision Events for Video REpresentation and Reasoning. ICLR 19
[3] ComPhy: Compositional Physical Reasoning of Objects and Events from Videos. ICLR 22
justification_for_why_not_lower_score: Novel framework and strong results!
Decision: Accept: notable-top-25%