Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The paper present a 3D-aware Global Correspondence Learning to tackle the virtual try-on problem. The method injects 3D priors onto the feature learning step via SMPL-based approach. The method shows benefits on using such an approach on two widely used dataset by the community
strengths_and_weaknesses: **Strengths**
- The paper is well written and the ideas are easy to follow
- Conducted experiments on two datasets, DeepFashion and MPV
- Ablation study on the proposed 3D-GCL framework
- Good qualitative results on the try-on clothes

**Weaknesses**
- The model depends heavily on the SMPL model estimation. 
questions: - If someone has a one-to-one correspondance from the DensePose, why would there be a need for the Stage I network . This is not to trivialize Stage I model, but rather to make sure that the simple waping filed obtained from DensePose are not enough and hence justifies its role. A baseline with only the one-to-one correspondance from the DensePose is much appreciated I think
- How the method deals with wrong semantic segmentation obtained from the human parser?
limitations: - As mentioned above, SMPL (DensePose) models are known to fail on hard posture especially on dance ones. How would the method deal on such cases? Relying solely on SMPL might give very wrong prediction, it would be good if the authors would develop on this
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This work presents a 3D-aware global correspondence learning (3D-GCL) framework to tackle the image-based person-to-person virtual try-on problem. The core idea is to incorporate the geometric prior of 3D human body to guide the correspondence learning between the source and the target person, aiming at preserving detailed garment textures. To circumvent the difficulty of learning long-range correspondence, the authors introduce a coarse-to-fine framework. The garment warping flow is initialized via the global correlation between high-level image features and then progressively refined. In addition, the SMPL flow estimated from the source and the target person is introduced as the 3D-aware supervision signal to guide the refinement process. Empirical studies on the DeepFashion and MPV dataset demonstrate superior performance over previous methods, especially on hard pose samples.
strengths_and_weaknesses: ## Strength

- The proposed method is simple and effective. It demonstrates solid improvement over previous methods in terms of FID and human evaluation scores.

## Weakness

- Missing comparisons. Some recent works also focus on incorporating 3D priors [1] or global correspondence [2] into image-based virtual try-on. There is no deep discussion on the difference between this work and [1] [2]. Quantitative comparisons with [1][2] are also missing. Comparisons with the above works are needed to elucidate the main contributions of this work.

- Unfair experimental setup. Line 224. "pairs without properly detected IUV maps or SMPL models are filtered out." I am concerned whether this is a fair comparison with other methods, as filtering out failure detections is beneficial for testing the proposed method. This makes the empirical results less convincing.

[1] ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors. ICCV 2021

[2] Style-Based Global Appearance Flow for Virtual Try-On. CVPR 2022
questions: 
- Typos: Line 76 summarize --> summarized
- What are the selection criteria for the HardPose test set? Is there a quantitative analysis of how hard the HardPose test set is compared to the public benchmarks?
- Abalation study Table 3. What is the framework like for 3D-GCL w/o global correspondences?
limitations: Yes. The authors addressed the limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The paper proposes to reconstruct 3D human mesh from multiview images by fusing features with a transformer and assisting the training by pose alignment between different views.
strengths_and_weaknesses: Strengths
- The writing of the paper is clear. 
- The motivation of the paper and the effectiveness of the proposed 3D-aware global correspondence is well supported by extensive experiments. 

Weaknesses

More ablation studies could be added to analyze the effectiveness of the components of the method.
- How does the multiscale/coarse-to-fine structure influence the final result?
- Can the off-the-shelf 3D mesh regressor be applied to (a)(b)(c) in Figure 3?  Will the SMPL flow supervision benefit these strategies as well? How does the module structure affect the warping compared with other module structures?

questions: See weakness 
limitations: yes
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This work proposes a virtual try-on technique to transfer garment from a given source image to target image/pose. A main difference to existing works is the use of dense correspondences between source and target poses and also using SMPL based regularization loss functions. Experiments on DeepFashion and MVP datasets with the proposed test splits demonstrate better results than existing techniques.
strengths_and_weaknesses: Strengths:
- SMPL based regularization losses for dense correspondence estimation.
- Better results on two different datasets compared to existing works on the proposed test splits.

Weaknesses:
- The main technical novelty of this work compared to prior art is the use of SMPL estimates for correspondence loss functions. As such the technical novelty is somewhat incremental. 
- Line 224 says that "image pairs without properly detected IUV maps or SMPL models are filtered out". This seems unfair filtering of test set as the proposed method relies on IUV map and SMPL model estimation. As a result, the reported numbers could be biased towards the proposed technique. Also it is not clear what it means by 'properly' here.
- The details on how the 'HardPose' dataset is constructed is not clear. What constitutes a hard pose? Having some visual examples that distinguishes normal and HardPose test sets would be good. And, I do not find any extreme poses in any of the result images. So, it is not clear to me whether any of the images can be considered a hard pose.


Post rebuttal:
Authors addressed some of the concerns, especially w.r.t. experimental setup, in their response.
questions: - The results reported in Tables 1 and 2 do not match to those reported in earlier works. I have checked some of the compared papers and their reported numbers seem different. Does this work use different test split compared to existing works?
limitations: There is no clear discussion of limitations of the proposed technique. For instance, the proposed technique fails when the dense pose estimation on either source or target images fail.
review_title: null

Meta-review: metareview: This paper received 4 positive reviews: 2xBA + WA+ A. All reviewers acknowledged that the proposed approach is simple and effective, it is well presented, and the claims are supported by strong empirical performance and extensive evaluation on several datasets. The remaining questions and concerns were addressed in the authors' responses, which seemed convincing to the reviewers. The final recommendation is therefore to accept.
confidence: Certain
Decision: Accept