Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: This paper presents a method for estimating the pose and shape of animals from images. The paper addresses limitations of previous approaches that cause misalignments and 2D keypoint locations. The main idea is to initialize using a parametric animal model and subsequently use a graph convolution network (GCN) to optimize for vertex positions to better fit shape segmentation. Experimental results how improved performance.
main_review: I would like to begin by listing several things I like with the paper.

- The problem addressed is important and gaining interest in the community.
- The paper presents several interesting ideas and identifies the key problem with using parametric models for image-based estimation tasks. The problems stated are not only a problem for animals but also for human parametric body models and estimation for human shape/pose from images.
- The paper is well written and experiments appear to be comprehensive and convincing.

In the following, I will focus on comments, questions, issues and suggestions to improve the paper (in no particular order).

- In figure 1, it is not immediately clear that the red and green masks denote false negatives and false positives. It would help to make this clear in the caption/legend.

- Related work: There's quite a bit of previous work that has recognized the issues that the paper has identified, i.e., using 3D models for image-based estimation results in misalignments at object boundaries because the 3D models do not model the image formation process.  Here are a couple of examples.

For humans, PiFU suggests tying 3D models to image pixels. This mitigates the misalignment problem.

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization, Saito et al.

More recent work proposes to model cameras explicitly (new work that appeared only recently).

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields, Niemeyer et al.

The paper also makes it sound like this problem is unique to animal reconstruction, but in it's really a large problem and points to important limitations of existing parametric models.

Furthermore, there is rich literature on convolutions on meshes which would be worth discussing. For instance, see:

MeshCNN: A Network with an Edge, Hanocka et al.

- The proposed "coarse" fitting method makes sense. For the mesh refinement, I would liked to see a bit more intuition for *why* the proposed GCN method is well suited? Why not some other method like predicting per-vertex correctives (as is common for faces) using a simpler neural net?

- My biggest issue with the paper is that it claims to refine both shape and keypoints, but very little details are provided for how the keypoints are refined. In figure 4, it is clear that the keypoints are improved, but *how*? There is a vague reference in lines 220, but other than that I see no other information.

- Line 221 says "estimated camera parameter f". Where is this estimated?

Misc.

- Line 137: "verctors" --> "vectors"


Overall, I believe the paper moves the state of the art forward in the field of animal shape/pose estimation, and maybe even has some lessons for human shape/pose estimation. There are some exposition and reasoning issues which I would encourage the authors to address in the rebuttal.
limitations_and_societal_impact: While limitations and broader impacts are discussed, these are limited to 2-3 lines. I would suggest expanding discussion of limitations, particularly discussing failure in figure 4, last row. Adding bolded subheadings would also make these sections more prominent.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper presents a coarse-to-fine pipeline to recover animal pose and shape. They first predict SMAL parameters and then use an MRGCN to refine surface vertices to achieve better image alignment. Their MRGCN follows an encoder-decoder structure, which is the main difference against previous GCN based methods. Their method achieves state of the art results on StanfordExtra, Animal Pose and BADJA datasets. 
main_review: ## Originality 
As far as I know, coarse-to-fine pipeline is very common in human pose and shape estimation area, and GCN is well investigated for human body or hand prediction. A refinement stage could certainly reduce the reconstruction error. So, the overall novelty of this paper seems low. 
However, it is the first time for me to see an encoder-decoder GCN structure, and the authors prove its ability in their ablation study.

## Quality 
This paper is easy to read except some spelling mistake (e.g. L.144, feed -> fed). They do a lot of experiments to show the effectiveness of each part of their pipeline. Their quantitative results achieve the best.
limitations_and_societal_impact: However, I feel like the qualitative results are not satisfactory, see my comments below.  

## Other Questions 
1.	Does this paper only estimate pose and shape of dogs? Although this paper claims ‘animal’ pose and shape estimation, I can see only dogs in Fig.1 and Fig.4. The training dataset StanfordExtra contains only dogs. I do not know their performance on other animals. 
2.	In Fig.4, these dogs have very different body shapes and surface geometry, however, I find the shape results in Column 5 are quite similar. As a comparison, qualitative results in WLDO (Fig.1 of their paper) demonstrate richer shape variability. I think this is a weakness of qualitative results.
3.	I would like to see an alternative view visualization of the models in Fig.4.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper studies the problem of joint animal pose and shape reconstruction from a single image. This work is largely based on the literature [Ref 2] but adopts a coarse-to-fine mechanism for joint pose and shape learning.  The method extracts global embedding as before in the first stage. In the second stage, this work leverages a local feature extractor to provide pixel-aligned local features for mesh refinement. Specifically, the extracted local features are given as input to the U-Net to refine meshes at a different resolution. Experimental comparisons with existing work [Ref 2, 3, 32] have been conducted on the StanfordExtra dataset and BADJA dataset.
main_review: Strengths:
* [S1] The proposed coarse-to-fine design makes sense and the paper is generally clearly written with a good amount of technical details and experimental setups.
* [S2] The proposed method achieves superior IOU and PCK@0.15 performances compared to the previous work. The qualitative results in Figure 4 look promising.

Weaknesses:
* [W1] While the 2D projections from the input camera view look decent, it is unclear if the actual 3D shape looks reasonable or not. The reviewer would expect to see the rotated shape from another angle as did in [Ref 2] (see Figure 5 and Figure 6 of [Ref 2]). It becomes difficult to evaluate the significance of the results without such information.
* [W2] Reviewer understands that the proposed method is regression-based, but would like two see if the test-time optimization could further push the limit. In addition, when running test-time optimization with keypoints available, is this method still superior to [Ref 2]? If so, how much performance gain comes from the coarse-to-fine refinement design? It would be good to have such discussions in the rebuttal.
* [W3] The reviewer would like to know if the second stage method is very sensitive to the first stage result. In other words, how does the first stage reconstruction affect the final performance? It would be great to provide some ablation studies on poor fitting on the camera parameter and/or SMAL model parameter.

limitations_and_societal_impact: Yes
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: - Paper focuses on 3D dog reconstruction from a single monocular image
- A coarse 3D dog estimate is generated using an existing pipeline (WLDO, SMALST), although with Tversky loss added to prevent oversized estimates (a known problem in prior work). 
- The paper introduces an encoder-decoder graph convolutional network which incorporates vertex-level local image features for applying free-form refinements to the the vertices of this coarse estimate.
- These are supervised using only weak 2D supervision and are shown effective for modelling dog shapes (which are very diverse).
- Ablation study + results on StanfordExtra, BADJA and AnimalPose show state-of-the-art performance.

main_review: **Strengths**

- Paper presents an effective approach to 3D dog reconstruction. This is a hard task due to shape diversity in the category, and approaches must rely on weak 2D supervision since 3D annotations are unavailable. Additionally the 3D morphable model (SMAL) is of low detail, having been constructed from scanned artist toys rather than real subjects. 
- I'm not fully convinced of the novelty of all parts, but the combination of ideas (refining coarse prediction with encoder-decoder GCN that incorporates local vertex information) is novel and particularly suitable for animal reconstruction. The contribution of these components are supported by results, although only using 2D metrics.
- Results appear of high quality, and are shown on diverse subjects with a range of challenging poses. However, results are shown only from the front view.
- Tversky loss from semantic segmentation task is proposed to avoid oversized SMAL estimates -- a problem in previous work.
- Paper is well written and easy to follow. Code was provided at time of submission.

**Weaknesses**
- The novelty appears limited. [Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels, Zhou et al.](https://arxiv.org/pdf/2006.04325.pdf) seem to describe an encoder-decoder graph network with up+down sampling layers for SMPL (see Figure 2 in the link). [Pixel2Mesh, Wang et al.](https://arxiv.org/abs/1804.01654) has demonstrated using local features in a GCN and [Convolutional Mesh Regression, Kolotouros et al. CVPR 2019](https://arxiv.org/abs/1905.03244) has demonstrated using a GCNs as a refinement network. Additionally, [Lions and Tigers and Bears; Zuffi et al](https://files.is.tue.mpg.de/black/papers/zuffiCVPR2018.pdf) show that using free-form vertex deformation (albeit via energy minimization) improves the accuracy of animal shape fitting. This seems to leave the novelty of this paper the combination of these techniques, their application to single-image monocular 3D dog reconstruction and supervising these architectures with weak 2D supervision only.   

- The authors compare their method on numerous 2D datasets (StanfordExtra, BADJA, AnimalPose) demonstrating quant/qual improvements over state of the art approaches. However, evaluating a 3D reconstruction using 2D PCK/IOU metrics, particularly rendered from the camera viewpoint is not ideal. For this reason, it is a shame the paper does not evaluate on 3D metrics which are available in RGBD-Dog, Kearney et al. CVPR 2020. 

- The paper contains only figures showing the reconstructed mesh from the camera's viewing direction. This can be misleading and causes some doubt when evaluating qualitative results. Reversed views of the mesh are needed.

- L158-163. Claim that oversized SMAL estimates caused by image foreground occupying a large area of the image after the cropping + this imbalance causes the network to estimate a mesh with larger size or larger focal length. I don't follow the logic here. The silhouette error is minimized if the predicted mesh matches the silhouette exactly. Why would preprocessing cause difficulties? However, I agree that the Tversky loss which penalizes false positive predictions, would resolve systematic issue of oversized predictions.

- L35 - Claim "we generate synthetic data for all animals, especially endangered species?" This appears untrue; the SMALST paper generates synthetic data on the Grey's zebra (an endangered zebra species) and obtains accurate results. 

- Paper title is 'ANIMAL pose and shape estimation' but results are only shown on dogs. Unless results are shown on other categories, this perhaps should be 'Dog Pose and Shape estimation'. 

**Minor**
- The authors refine the vertices using graph convolutions. However, another implementation (albeit to refine parameters) shown in [Human Mesh Recovery; Kanazawa et al., CVPR 2018] is to use [recurrent steps](https://github.com/nkolot/SPIN/blob/master/models/hmr.py#L140). A discussion on the differences would be useful and interesting.
- Paper shows a failure case which they explain is due to the camera looking at the back of the animal. This is a hard case, and the authors put it down to a lack of views in the training data. However, I wonder if this could be better handled in modelling the ambiguity in 3D reconstructions. Perhaps work like [Probabilistic 3D Human Shape and Pose Estimation from Multiple
Unconstrained Images in the Wild; Sengupta et al] and [3D Multibodies; Biggs et al. NeurIPS 2020] would help? 
- The paper proposes the Laplacian regularizer to constrain the free-form vertex deformation step. This differs from ARAP used in [Lions \& Tigers \& Bears, Zuffi et al, CVPR 2018]. Could the authors explain the choice here?
- Is your training/test split the same as published in the WLDO Github repository?

- Some typos e.g. L144  `"global feature is FED", L158 "we RECKON" (informal language), L266 - capitalize WE. Possibly others, have not checked exhaustively. 

**Review score**

This paper demonstrates an effective approach for a challenging 3D reconstruction task, focusing on an articulated category with limited training data and not a huge amount of prior work. I'm not convinced of the novelty of any of the individual components (encoder-decoder GCN, free-form refinement, local vertex features, Tversky loss all seem to be prior art). However I believe the combination of these techniques to be non-trivial, and they are shown to be effective in modelling dog shapes (which are much more diverse than humans and have only 2D supervision).

The experimental section would benefit from having side-views and evaluation on a 3D dataset (e.g. RGBD-Dog) which is now available. However, previous animal papers have not conducted a 3D evaluation so I don't think this is enough to reject this paper.

All things considered, I believe this paper is interesting and pushes the field of 3D animal reconstruction forward. I am on the fence, but overall I consider this paper just about worthy of acceptance to NeurIPS.
limitations_and_societal_impact: Limitations indicated above. Societal impact is discussed briefly in conclusions.
review_title: null

Meta-review: The paper received 4 positive final ratings: 7, 6, 6, 6.
On the positive side, the reviewers appreciated importance of the problem, an overall meaningful approach and strong empirical performance. The main remaining concern was around novelty of individual components, but at the end the reviewers agreed that the combination of those is sufficiently novel and effective in the given setting. The remaining concerns were mostly around clarity, gaps in related works, and somewhat limited evaluation (dogs only). Some of those were addressed in the rebuttal, as acknowledged by the reviewers.
The final recommendation is to accept as a poster. The authors are highly encouraged to incorporate all feedback from the reviewers in the camera ready version of the manuscript.
Decision: Accept (Poster)