Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a method to model chirographic data using diffusion models. Continuous-time geometric data (i.e., chirographic data) is typically modeled via autoregressive methods which fail to capture holistic information of the temporal data. On the contrary, the proposed ChiroDiff is capable of capturing holistic concepts and remaining resilient to higher temporal sampling rate. To my knowledge, this work is the first to model chirographic data with diffusion models. Both quantitative and qualitative experiments have been conducted to verify the effectiveness of the proposed method in several different applications. 
strength_and_weaknesses: Strength:
Recently, the superiority of diffusion models has been proven in the application of image synthesis. However, no work has been done to demonstrate the effectiveness of diffusion models in sequential data synthesis. This paper seems to be the first work to effectively using diffusion models to synthesize chirographic data. Therefore, I think many researchers will be interested in this paper, which makes some valuable academic contributions to the community. Despite some tiny representation problems, the paper is generally well-written and easy to follow. 

Weaknesses:
1) The whole Section 3 provides a detailed description of DDPM, which is already well-known in the literature. I think this section should be shortened, removed, or integrated into Section 4 to emphasize the technical contribution of the proposed ChiroDiff.
2) Tables with accurate evaluation values should be provided instead of just displaying plots (Fig. 5 and 6). What is worse, no qualitative comparison has been made, and no user study has been conducted. In fact, for the tasks of image/graphic synthesis, qualitative evaluation is often more important than quantitative comparison. Many people want to directly compare the performance of two image/graphic synthesis methods by inspecting their synthesis results. Therefore, I suggest showing more qualitative results obtained by the proposed ChiroDiff and other existing approaches in the manuscript and its supplemental materials. Only in this manner, the advantage and necessity of modeling chirographic data via diffusion models can be clearly verified.

clarity,_quality,_novelty_and_reproducibility: The clarity of this paper can be further improved by carefully revising the method description sections. The novelty is good. The authors are encouraged to release their code since it is not easy to reproduce the proposed method through reading those method descriptions and formulas. 
summary_of_the_review: The authors deserve the credit since they are the first to introduce diffusion models to handle the interesting task of chirographic data synthesis. However, there still exist some weaknesses in this paper as mentioned above. Otherwise, I could give it a higher score.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This work proposed a method for generating online handwriting data based on diffusion models. On several datasets, the proposed method outperforms previous approaches.
strength_and_weaknesses: Strength:
- To my best knowledge, this is the first work that explores diffusion models to generate temporal sequences.
- The proposed method overcomes the limitations of autoregressive models and achieves better empirical results on several datasets.

Weakness:
- The method directly adopts a previous diffusion model (DDPM) by replacing UNet with GRU. Thus, the technical novelty of the method is limited.
clarity,_quality,_novelty_and_reproducibility: Clarity: good.
Quality: good.
Novelty: It is a novel application of diffusion models.
Reproducibility: good.
summary_of_the_review: Although the technical novelty of this work is limited, it is the first work that applies diffusion models to generating temporal data and achieves promising results on several datasets.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes the use of diffusion models (DM), more specifically the Denoising Diffusion Probabilistic Models (DDPMs), for modeling chirographic data such as digital handwriting, sketching, and drawings. In contrast to the mainstream autoregressive approaches on sequential chirographic data, the proposed method, CHIRODIFF, processes the entire sequence in one go and hence better captures holistic concepts over sequences. The paper demonstrates the benefits of the proposed method in a variety of tasks, including de-noising, interpolation, and temporal reconstruction of images or 2D point clouds. CHIRODIFF is also compared with various architectures implementing different model classes, and it achieves better or competitive performance in reconstruction and generation tasks on the VectorMNIST, KanjiVG, and a subset of the QuickDraw datasets.
strength_and_weaknesses: Strengths:

1- The paper applies a more powerful and expressive model class, the diffusion models, to chirographic data.

2- The paper is well-motivated around the holistic view achieved by the diffusion model. I think it is a novel approach in terms of how the sequential chirographic data is treated.

3- The proposed model has an interesting interpretation. At T, before any reverse diffusion steps, the chirographic structure is represented as Brownian motion. It basically treats every timestep as an independent data point by ignoring the causality. Nevertheless, it learns to transform this unstructured representation into realistic data samples. This introduces another interesting property that the model can handle sequences with arbitrary lengths and sampling frequencies (at least in theory; see the first weakness related to this). 

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Weaknesses:

1- One of the main claims of this work is the proposed model’s inherent advantage for modeling continuous-time data. I am not sure if this is entirely correct, as the backbone architecture is a Bi-RNN that processes the sequence step-by-step to approximate the noise. How could CHIRODIFF be better than the baselines like SketchODE or the decoder of CoSE? In particular, the latter can reconstruct a sequence at arbitrary resolution. Isn’t the underlying Bi-RNN prone to failure when the sequence length is arbitrarily increased (i.e., out-of-domain problem)?

2- In the stochastic vectorization task, the reconstructions are slightly different from the perceptive input. I am not sure if this is the expected result. While the model could capture different temporal modes and preserve semantics, it changes its appearance. Could it be due to the quality of the latent condition z or the stochasticity of the diffusion model-based decoder? 

3- In contrast to the auto-regressive approaches, the proposed model lacks the ability to complete partial drawings. I think this is perfectly fine but it should be discussed in the paper.

4- This is not a weakness per se. Considering that the paper doesn’t introduce major modifications to the original diffusion model formulation, the background section on diffusion models seems a bit redundant. Some space could be saved for additional experiments, especially for ablation on the model’s performance on more complex data such as the hand-drawn diagram dataset of Gervais et al. or on computational efficiency (i.e., sampling-time vs. increasing sequence lengths, design choices, etc.).

clarity,_quality,_novelty_and_reproducibility: The paper is well-motivated and well-written. The contribution claims are supported by empirical evidence. Though technical novelty is limited, the application of the diffusion models in this particular domain is novel and it introduces an interesting approach.
summary_of_the_review: I find this paper useful for the community. It introduces a new model class and compares it against a carefully chosen set of baselines that implement different modeling concepts. Despite the advantages of the underlying diffusion models, there are also some weaknesses, which should be discussed in the rebuttal and incorporated into the paper. I am leaning toward accepting this paper if the authors provide clarifications in the rebuttal and address my concerns. 

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**Post rebuttal update:** The rebuttal and the proposed revisions address my concerns. Hence, I am in favor of accepting this paper.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper uses a denoising diffusion probabilistic model (DDPM) to model chirographic data (i.e. sequential vector strokes, as in handwriting or sketching). In prior work, autoregressive models have dominated this space. DDPMs offer potential advantages, in particular avoid the problem of exposure bias / autoregressive drift.

Strengths: reviewers all generally agreed that this topic is timely and would likely be of interest to many people in the ICLR community. It could inspire future work in applying DDPMs to other kinds of mixed continuous/discrete sequential data. The paper is technically sound and well-executed.

Weaknesses: reviewers noted the lack of qualitative comparisons to prior work and the lack of a perceptual study. Some reviewers thought the paper lacked novelty, as it could be viewed as "just" a DDPM with the U-Net replaced with a GRU.

The initial scores for this paper were all on the accept side. The authors' response did a good job of answering reviewer concerns, and reviewers remained in favor of accepting the paper.
justification_for_why_not_higher_score: While the paper adds an interesting and useful point in the design space of sequence models for chirographic data, and it is likely to be of interest to many people + inspire some future work, there is not huge technical innovation here, nor does the paper present results that are so significant that they should be highlighted to the entire ICLR community.
justification_for_why_not_lower_score: "Accept (poster)" is the lowest accept score, and all reviewers were in agreement that the paper ought to be accepted.
Decision: Accept: poster