Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposed differentiable yarn-level dynamics for fabrics. Compared to the previous differentiable cloth model, this paper is more specific and fine-grained. They carefully design the shear force and Coulomb friction force so as to keep the physics accurate as well as differentiable. The experiments show that their method can learn the control policy and material parameters successfully.
main_review: The modeling of this differentiable physics paper is very fine-grained compare to prior papers. Their contact dynamics can even model the Strobeck effect and also simulate self-excited vibrations. I would very much like to see those detailed dynamics in their video. (By the way, the authors mentioned the supplementary video in the paper several times but I could not find the supplementary materials. Did the authors upload it? Or is there something wrong with my reviewer console?)
The authors also conduct extensive experiments to support their claims. 

I also have some concerns after reading the paper,
1. The fine-grained model is accurate. I wonder what its time performance is. Could the author report the wall clock time? I also notice the time step is relatively small (0.001s). What is the biggest time step the simulation can deal with?
2. How this method can scale to some more complex topology? All the experiments are on one square sheet. Can your method simulate something like a T-shirt or dress, which is probably more relevant to design and fabrication?
3. Could the author release the code and video as supplementary materials?

summary_of_the_review: This paper presents a more fine-grained model compared to other differentiable physics papers. Claims are well supported by the experiments. It would be better if the author can better demonstrate their application to more complex scenarios (like a T-shirt or dress instead of a single square sheet).
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This manuscript enhances the yarn-level cloth simulation to a differentiable level by introducing alternative differentiable operators in place of originally discontinuous/indifferentiable counterparts. In addition to differentiable components for energies, collisions, frictions, etc, this manuscript also differentiates through the implicit Euler method, which enables greater time steps with better stability. The authors empirically studied the efficacy of this differentiable simulator on a variety of environments with multiple baselines. I believe one of the major contributions can be a non-trivial implementation in C++ and its interfaces with modern deep learning architectures (unsure if it will be open-sourced through).
main_review: I believe the implementation of this paper is respectful and non-trivial. It looks like that this paper fills the gap in differentiable simulation with a cloth simulator from a yarn-level perspective. Despite its potential applications, I have a few technical questions and suggestions about it:

1. This manuscript provides a novel view of the intersection of differentiable simulation and cloth simulation by thinking at the yarn level. I believe one of the important baselines is [1], which was the latest work working on differentiable cloth simulation in a mesh-based view. I expect some discussion between this work and [1].
2. The statement "Previous research (Liang et al., 2019; Du et al., 2021) suffers from this problem, which unfortunately leads to learning implausible parameter values" looks technically inaccurate to me. Actually, leveraging prior knowledge for physical parameters is now a necessary step for system identification tasks. For example, because of the huge numerical difference between Young's modulus and Poisson's ratio, [2] proposes a logarithm transform to alleviate the unbalancement.
3. I am curious about the wall-clock time and memory footprint of the experiments. It will be helpful to note them.
4. As I questioned in the summary: a physical simulator usually comes with a steep learning curve which disables following works by the implementational difficulty. I wonder if the source code will be released for future follow-up.
5. My main concern is motivation. The yarn-level simulation was proposed for more accurate collision handling and detailed presentation. However, the experiments are too coarse to show these advantages, which makes me question why the yarn-level simulator is used. As a reference, [3] used much more yarns to represent their cloth. I expect a more valid explanation for motivation or a set of better results that are more complex than 25*25.

[1] Li, Yifei, et al. "DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact." arXiv preprint arXiv:2106.05306 (2021).

[2] Hahn, David, et al. "Real2sim: Visco-elastic parameter estimation from dynamic motion." ACM Transactions on Graphics (TOG) 38.6 (2019): 1-13.

[3] Sperl, Georg, Rahul Narain, and Chris Wojtan. "Homogenized yarn-level cloth." ACM Trans. Graph. 39.4 (2020): 48.
summary_of_the_review: This manuscript proposes a combination of yarn-level cloth simulation and differentiable simulation and shows the application of system identification and control. The thing I am worried about is that the motivation of using yarn-level, which is originally designed for fine details but applied on over-simplified models. I would like to know more about why the authors chose the yarn-based method, how they would like to use this simulator, and where they see the simulator fits in. Generally, I appreciate the work but was not convinced by the motivation. I am open to changing my score based on the clarifications I get.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper introduces a model for yarn-based fabric simulation that is differentiable and hence particularly well suited for solving inverse problems, such as deriving model parameters from reference video input. 

The technical contribution itself (execution of the forward model) does not necessarily fall into the realm of machine learning but it is nicely embedded in an inverse framework and demonstrated on a scenario that is clearly computer vision. Hence, I would consider the paper on-topic for the venue.


main_review: Pluses:
* The authors identify a major shortcoming of previous simulation models, namely the use of discontinuous force terms, and engineer alternative models that approximate the terms from domain literature but have the added benefit of being smooth everywhere. I have to say I quite like this general approach of searching for the sweet spot between textbook physics and usability of the model for solving secondary problems.

Questions:
* You write that the non-linear stick-slip force term lends itself to oscillatory behavior. Without being familiar with such simulations in the slightest, I feel that this could lead to free energy being trapped in entropy and therefore thermal loss. Did you ever encounter such behavior?

* Why do differences between inter/intra parameters (Table 2) and ground truth as the grid size increases? Should it not be the other way around?

Weaknesses:
* In the Comparison video, I could not spot any significant difference between the different weaves. At least not more than between Ground Truth and Ours. Did you also experiment with a bit more extreme differences in fabric?

* It seems that no real-world experiments were done. The range of simulated experiments is not very wide, so I can't judge the expressivity / gamut of the model.
summary_of_the_review: Again, I am no expert, but I think this is a paper that fits its time. It appears to be well executed and evaluated. Whether or not the works falls under "machine learning" is probably a matter of personal taste. The contribution is a simulation that serves as forward model component in a regression framework, so I think that should qualify it.

As the authors acknowledge, neither the specific problem nor the general idea of differentiable simulation is new. Whether the specific solutions introduced in the paper generalize to other problems is unclear. Still, I think this is reasonably nice work to warrant acceptance.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper studies the fine-grained cloth simulation using differentiable physics models. The paper is largely based on the prior work on cloth simulation [Liang et al., In NeurIPS 2019] but with the domain-specific yarn-level formulation. Specifically, it proposes to represent cloth as two perpendicular groups of parallel yarns. Built upon this representation, the paper introduces several differentiable strategies to model inertia, internal and external forces. Experiments have been conducted on simulated data for material estimation and model-based control learning.
main_review: Strengths:
* The paper studies a challenging topic in differentiable physics, namely, the fine-grained differentiable cloth simulation.
* The paper contains detailed problem formulation and derivations.
* The paper demonstrates promising results on the synthetic data compared to the baselines [Liang et al., In NeurIPS 2019] and Bayesian optimization on material estimation.

Weaknesses:
* The paper does not seem to comment on the computational cost and comparisons to baseline methods. It would be good to provide such information in the main paper, especially when compared with non-differentiable simulator [Cirio et al., 2014] and prior work on differentiable cloth simulation [Liang et al., In NeurIPS 2019].
* The quantitative results can be presented in a clearer manner. For example, it would be good to show the material prediction error in percentage in Table 2-4 (rather than the absolute value).  It would be good to include the algorithm (refer to Section 3.1 in [Liang et al. 2019]) in the main paper. 
* It would be good to know whether the proposed differentiable simulation is deterministic or not? Whether the material estimation is sensitive to initialization? In this case, please consider reporting the standard deviation for multiple runs of the same setting.
* It is unclear what happens if the parameter prior was not provided to the proposed method in the control learning experiment. It would be good to provide such ablation studies and discuss the results in the rebuttal.
summary_of_the_review: Overall, this is an interesting paper with solid technical contributions. The main weaknesses include the presentation and experimental evaluations.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This submission proposes a differentiable parametric force model for fabric, at the level of yarns, by modelling interactions forces between yarns. Since some of these forces are not differentiable, the authors propose a differentiable approximation for them.
As a results, gradient descent can be used to recover physical parameters, by fitting the parametric model to a dynamic sequence of frames. Differentiability is also used to optimize forces for control: gradient descent can optimize the required forces to apply to a piece of fabric to throw it in the air and aim for a target position.

main_review: Strengths:
- The proposed model allows to estimate physical parameters efficiently, ie. :
    - from a small sized fabric only (then simulations can be carried on a larger cloth size) (Table 2)
    - from a few frames only (table 3)
- The comparison of (a) gradient descent on the proposed model with (b) bayesian optimisation and (c) a sheet model turns favorable for (a) in terms of error + convergence speed.
- The force model is extensive (inertia, stretching, bending, contacts between yarns, friction, shear, collision between parallel yarns, gravity, wind…)
- The submission is well grounded in previous literature for yarn modelling
- Supplementary videos are a plus
- Nice figures and good readability 

Weaknesses:
- In 4.1 the “Learning” experiments should rather be called “fitting” a few parameters of the model. Indeed the applicability is very narrow: the prior on parameters is strong (restricted range), the model is only able to fit data for which there is a solution (it is a simulated yarn fabric) and for which we have the exact positions of the intersections and the number of yarns. This setting is unrealistic. What would happen if we were to fit a 3D mesh, or a 3D sparse scan, for which the positions q_i are not available?
- An ablation study is missing: what would happen if the proposed differentiable forces were removed? Showing such a result would prove the relevance of devising a differentiable approximation for them
- Fitted parameters $\omega$ should be re-stated at the beginning of 3.4
- The scalability claim does not really hold: the largest demonstration (support video) is for a 70*70 yarns which is still very crude.


Minor typos:
- End of introduction “… and fast in control learning”: it looks like a word is missing
- Related work / differentiable physics simulator “simulaiton” -> simulation
- Beginning of Methodology (+repeated): “system equation” -> system of equations?
- In 3.2 the dimension $l=3rx+2(r-2)(c-2)$ is not equal to what is stated in 3.1 : $(r-2)(c-2)$ crossing nodes with 5 DoFs and $2r+2c$ with 3 DoFs gives a dimension of $l’=l+12$

summary_of_the_review: This paper proposes a new differentiable force model for yarn fabrics which is novel and able to fit clean data for which the positions of the yarns are known. Yet the experiments fail to prove it is useful to fit general fabrics in a less controlled setting.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a new differentiable physics-based model for simulating composite materials such as fabrics in cloths.  This is approached by formulating  several common differentiable forces exhibited in yarn-to-yarn interactions in different waving patterns.  Different from previous generally-purposed physics model, the proposed method is the first data-driven fine-grained yarn level modelling that takes into account complex structure/topologies and force interactions.  It models fabrics with composite materials and slim patterns and proposes new differentiable forces on/between yarns. Experiments show the efficacy of the proposed model, confirming the model’s capability for simulating heterogeneous materials such as fabrics.


main_review: 
Strengths: 

This is the first fine-grained data-driven yarn-level model for describing internal forces for fabric. The differentiable nature of the forces allows this model to be incorporated in a learning-based cloth simulation framework, handling complex physical structures and heterogeneous materials at the yarn-level.   Experiment results show the model achieves significantly lower errors than previous methods and has high data efficiency.  Although only cloth simulation is demonstrated , the model can have wider applications in fabric manufacture and design areas.  The underlying principle may be extended more generally for other related tasks.  


Weakness:   

1. The paper incorporating some prior knowledge about the parameters’ range in order to mitigate illposedness.  However, the entire design process  is rather heuristic: neither analysis nor ablation study is presented to justify how such those design choices were made.  

2. In order to illustrate data efficient of the proposed method,  the authors trained the same model on different frame numbers ranging from 5 to 25 for low-dynamics motions.   There is no mention of how the method performs as a function of the magnitude of the forces.  Additional experiments can be done and should be done. 

3. In paragraph-2 of section 4.2.1, the paper claims “When the training data increase from 5 frames to 25 frames, the error reduces by around 100 times.  However, in Fig-6 left, the reduction of error is only 10-fold.  Similarly in Table-4,  the error is reduced only by about 10 times rather than 100 times, unless I misread this section. 

4.  Experiments have been focused on simulated woven cloth.  Yet, the models are heavy-handedly designed. This casts some doubt regarding the generalizability of the proposed method. How does  the proposed force model on other different materials and weaving structures is unclear. 

summary_of_the_review: Despite  that the experiment section of the paper can be improved in many aspects, I think the core contribution of the work is new and valuable, and the paper is acceptable condition on some minor revision.
review_title: null

Meta-review: This paper introduces a differentiable yarn-level model of fabrics. The model is more detailed and physically realistic than proposed in earlier work, which may allow for applications to manufacturing guidance and textile design.
The paper is generally well-written and contains detailed problem formulation and derivations.
Experiments show it is possible to successfully learn a control policy and material parameters using the differentiable model.
Decision: Accept (Poster)