Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposes a 3D-CoreNet  to solve the 3D pose transfer problem. The 3D-CoreNet consists of two steps: a correspondence learning step and refinement step. Firstly, the authors treat  the shape correspondences learning problem as  an optimal transport problem, which can be solved without using any correspondence annotations. A coarse warped mesh can be obtained from the learned correspondence. Then, an Elastic INstance Normalization (ElaIN) is introduced to the refinement module in order to blend statistics of original features and the learned parameters from external data. State-of-the-art performance has been achieved with the proposed 3D-CoreNet.


main_review: Pros:
- Performance. The proposed 3D-CoreNet achieves state-of-the-art performance.
- Clarity. The paper is well written and easy to follow.
- Technical novelty. The main contributions of this paper are two folders: 1) propose to learn correspondences for a coarse estimation such that the output meshes are aligned with the identity meshes; 2) Introduce the ElaIN from image style transfer task to the 3D pose transfer task. 

Cons:
- Missing references and experimental comparison.
 1. [1] Keyang Zhou et.al. Unsupervised Shape and Pose Disentanglement for 3D Meshes. ECCV2020.
 2. [2] Wang Yifan et.al.  Neural Cages for Detail-Preserving 3D Deformations. CVPR2020. 
- Evaluation dataset. The authors generate their own dataset for training and testing. Is it possible to directly use the dataset from previous work [37] and compare with their results? 

Further comments:
- Time complexity of the proposed approach. How to decide the number of iterations in the Sinkhorn algorithm? And how does it affect the time complexity in comparison to existing works?
- Ablation study on the correspondence learning step. One of the main contributions of this paper is the introduction of the correspondence learning step for coarse estimation. It would be better to also include the ablation study for this step (the backbone might be similar to [37] except for the ElaIN).
limitations_and_societal_impact: The authors have discussed the limitations and potential negative societal impact in the supplementary.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The paper proposes a reposing mesh algorithm based on identity and posing mesh, specifically, they learn the shape correspondence by optimal transport problem, and proposes lastic instance normalization to refine the generated mesh. Thus they have satisfying mesh visualization. 
main_review: The visualization of the paper is satisfying and the proposed contribution is working and making better results both qualitatively and quantitatively. 
Why is line 142 'Z=1-C'
Can you show more of the learning correspondence in simplified mesh so that we may understand more of the semantic meaning and can understand better of the corresponce?

limitations_and_societal_impact: Lack of related work like 
'continuous surface embedding'
FULLY CONVOLUTIONAL MESH AUTOENCODER USING EFFICIENT SPATIALLY VARYING KERNELS
can you discuss more differences between the submission and related works?

Line 35: ' Our method does not require the two meshes to have the same number or order of vertices.', does this apply to the same person but with different vertices (say one input mesh is simplified?)
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper tackles the problem of 3D pose transfer: given an identity mesh Mi and a posed mesh Mp, the key problem is to output a mesh M' with the same id with Mi and the same pose with Mp. The proposed method is in a coarse-to-fine manner. It firstly ultizes optimal transport to learn a coarse correspondence, with which a coarse mesh can be generated. After that, a novel "elastic instance normalization" operation is proposed for mesh refinement. 
main_review: The strengths include:
1) The motivation is very clear and the proposed algorithm is also well-designed. The coarse-to-fine strategy for 3D pose transfer makes sense. 
2) The experiments well validates the effectiveness of the proposed methods. 

There are still some weaknesses:
1) I am not very clear that if the proposed optimal transport formula is novel or not for correspondence learning. If it is not novel, the contribution is limited. If it is novel, I think the comparisons on correspondence learning part are lacking. 
2) The primary contribution to me is the proposed novel "elastic instance normalization(ElaIN)". However, the experiments to verify the effectiveness of ElaIn is not enough. For example, the authors can replace SPAdaIn with ElaIN in [37] to show if ElaIN can also benefit the method in [37]. 
3) Based on the above thoughts, I think the technical novelty seems not that significant.   
limitations_and_societal_impact: No more comments.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper investigates transferring the pose of a source mesh to a target mesh while keeping its identity. Given a source pose mesh and a target identity mesh, the proposed method first learn the shape correspondence by solving an optimal transport problem and then refine the mesh with the proposed Elastic Instance Normalization. Both quantitative and qualitative results are strong on both human and animal mesh transfer.

main_review: The paper proposes a COrrespondence-REfinement Network (3D-CoreNet) for solving the pose transfer problem of both the human and animal meshes. Similar to [37], the proposed method does not need correspondence annotations of the inputs. In 3D-CoreNet, the network uses the optimal matching matrix instead of the correlation matrix to learn the correspondence. After obtaining a coarse warped mesh, the network further refines the warped mesh with the proposed Elastic Instance Normalization (ElaIN), which is shown to be more effective than the SPAdaIN in [37]. 

Overall, this paper includes new technical contributions for mesh pose transfer with comprehensive experiments and convincing results. Other than that, the proposed method is an incremental update over previous work [33,37]. Some aspects also need further improvements.

- The paper claims that ElaIN is more effective than SPAdaIN, which is also supported by experimental results in Table 2 and Figure 5. These results are evaluated on the final pipeline with the warped mesh. Adding results of directly replacing SPAdaIN with ElaIN in [37] and making a comparison with the results of [37] should be more straightforward. 

- The proposed 3D-CoreNet needs to compute correspondences by minimizing the total matching cost in Eq. (2). What is the time cost compared to the alternative solution using correlation matrix and the baseline [37] without correspondence learning?

- Adding a visualization of the learned correspondences between identity and pose meshes should make the experimental results more comprehensive and convincing.
limitations_and_societal_impact: Though the results of the proposed method shown in the paper and supplementary material are quite well, adding discussions about the runtime of the 3D-CoreNet should strengthen the paper. For other concerns, please refer to the Main Review.
review_title: null

Meta-review: The reviewers identified a number of strengths and weaknesses in this submission. All scores are in the borderline range, but ultimately all reviewers recommend that the work should be accepted. I see not reason to overrule the reviewers in this respect and therefore recommend acceptance. 
Decision: Accept (Poster)