Summary and Contributions: This work proposes a generative model for textured 3D meshes based on the popular GAN framework. At its core is the idea of parameterising both texture and surface as 2D representations, so that to handle them with 2D convolutional GANs. Interestingly, supervision is obtained by leveraging recent advances in single view reconstruction through differentiable rendering.
Strengths: In my opinion, the most interesting contribution of the paper is in the proposed pipeline to generate data for training the generative adversarial network: - I find the idea of using SVR reconstruction pipeline to generate "ground truth" geometry in an unsupervised fashion to be clever, and interesting for the community at large - Similarly, the idea of generating pseudo-ground truth textures by projecting the ground truth image onto the UV map using a simple orthographic projection is interesting and relevant for future work towards generation of textured 3D surfaces
Weaknesses: Qualitative results, especially for the car category, show that there is still some work to do to achieve the generation of high quality textured 3D surfaces: - The first clear problem is in the ground truth geometry: the proposed representation can only capture fixed topology (i.e. the one of a sphere) and struggles at modelling high frequency details. It would interesting to see which improvement could be achieved by using a stronger prior on geometry (as suggested by the authors in the conclusion, enforcing a stronger prior on geometry through synthetic datasets would be helpful - e.g. pre-training the decoder on shapenet cars) - I wonder how the simple orthographic projection models impacts the alignment of geometry and texture, as the applied transformation differs significantly from the "real" one. Could the author give insight on this?
Correctness: Yes, the pipeline is correct, novel, and interesting.
Clarity: Yes, the paper is well written and easy to follow. The supplemental video further demonstrates the result achieved by the proposed pipeline.
Relation to Prior Work: The related work section analyses related work in detail.
Additional Feedback: Thanks to the authors for their response, which addresses my concerns.
Summary and Contributions: Recently, inferring 3D geometry of objects has drawn great attention while generating textured meshes for objects still lacks research studies. This work is a pioneer attempt. To simultaneously generate the mesh vertices and texture maps, the proposed method utilizes a unified uv-parameterization to encode the appearance for texture and displacement map for geometric information. The displacement map is defined over a given template sphere mesh. The results look appealing.
Strengths: - This is really a timly work which is a pioneer attempt to generate textured meshes from a single-view image. - The proposed network architecture is novel to me. It also makes a lot of senses. Using uv-map for textured mesh generation is a smart strategy. The results also demonstrate the effectiveness. - The obtained results seem appealing to me which are obviously better than previous works I have seen.
Weaknesses: - My major concern is the lack of comparisons against other methods. In my knowledge, there are several baselines: 1) generating 3D voxels with color channels using 3D CNNs; 2) in pix2mesh framework, adding a separate branch to infer the color for each vertex. There are no comparisons against such baselines, thus it is not clear how does the proposed method perform. - There is an obvious limitation that the proposed method cannot deal with the shapes with complex topologies. The examples shown in the paper are all of simple structures. I hope to see whether the method can work for complicated objects like chairs. I guess this would be very challenging. Although there are these weaknesses, the proposed approach well advances such a emerging and timely research topic.
Correctness: Yes, they are correct to me.
Relation to Prior Work: Yes.
Additional Feedback: Post-rebuttal: I am also satisfied with the rebuttal, it addresses most of my concerns. Even the current version has some limitations it cannot handle complex shapes), it can be a good starting to trigger future research in this direction.
Summary and Contributions: The paper presents a framework to generate triangle meshes and texture maps which can be learned using single-view natural images. Given a set of 2D images, the images are augmented with mesh estimates and subsequently converted to pose-independent 2D representation via UV-projection. Finally, a 2D GAN is learned to generate 3D meshes where, given a random vector, the generator generates displacement maps and textures, and discriminator discriminates between real/fake displacement maps and textures. Experiments are performed on CUB and Pascal-3D+ datasets.
Strengths: - The generation of 3D meshes is a challenging problem and of great interest to the community. - The proposed approach is novel and makes sense. - The proposed convolutional mesh representation is novel and intuitive. - I like the rationales behind choosing UV-sphere for mesh representation. They are well motivated and intuitive. - I like the usage of positional encoding which yields higher FID scores as shown in the ablations studies. - The proposed approach can generate meshes conditioned on class labels, attributes, and labels. - The qualitative results provided in supp. material look very good. - Sufficient experiments are performed to satisfy design choices. In summary, it is a very good paper and will definitely justify acceptance to NeurIPS.
Weaknesses: - I cannot find any signficant limitation of the paper.
Clarity: - The paper is well written and easy to read. - Sufficient implementation details are provided. - The authors will release the source code which solves the reproducibility problem.
Relation to Prior Work: Yes. Some remarks: Line 26-28 "Provide a limited degree of control over pose": The statement is not quite true and the following papers should be cited: HoloGAN: Unsupervised learning of 3D representations from natural images, ICCV'19 Self-Supervised Viewpoint Learning from Image Collections, CVPR'20 HoloGAN shows that they can obtain control over poses in many diverse scenarios.
Additional Feedback: Typos: Line-134: consists in -> consists of ## Post rebuttal. I didn't have any critical remarks in my initial reviews. Other reviewers also agree that it's a good paper and should be accepted to NeurIPs. I keeping my original rating.
Summary and Contributions: This paper proposes a generative adversarial network for generating 3D meshes and textures. It first trains a network to predict meshes and textures from a single image, and then it uses the meshes and images to generate ground truth training data to train a GAN. The experiment results show that the proposed method can generate new textured 3D meshes from sampled latent code and text descriptions.
Strengths: 1. The method is able to generate new 3D meshes with textures from sampled latent codes and text. 2. The method uses 2D displacement maps to represent 3D meshes, which enables it to use 2D convolutions to generate 3D contents.
Weaknesses: 1. While I find the combination of single-image 3D reconstruction and GAN interesting, I am concerned about the technical contribution of the paper. It seems that each component is similar to previous works. The single-image 3D reconstruction network is almost identical to , and the GAN network also are standard. It feels like the contribution of the paper is just a combination of these two tasks. 2. Another solution to the proposed task here is that first training a 2D GAN to generate new 2D images of specific category, and then directly run the single-image reconstruction network such as  to generate textured mesh from the input image. The paper should include a comparison to this baseline. My sense is that currently GAN can generate very high-quality 2D images from sampled latent codes and text. It should be easy to directly generate resonable textured meshes from the high-quality 2D images. It is not clear to me why the proposed framework would outperform this baseline, considering that the performance of the proposed method is also bounded by the performance of single-image 3D reconstruction network. In addition, this alternative solution would be more flexible than the proposed method, since you can use arbitrary GAN network to generate 2D images without re-training the reconstruction network. 3. Why using the sinusoidal encoding in the network? How does it compare to directly using the (u, v) coordinates? Overall, I like the results of the paper. However, I am not fully convinced about the choice of the framework, particularly for the questions discussed in point 2. The technical contributions of the proposed method is also not significant to me.
Correctness: The paper is technically sound.
Clarity: The paper is well written.
Relation to Prior Work: The reference is good.
Additional Feedback: Post-rebuttal: The rebuttal addresses my concerns, and I increase my score and agree for accepting the paper.