Paper:
MULTI-VIEW DATA GENERATION WITHOUT VIEW SUPERVISION
The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial neural networks. Different variants have been proposed where the underlying latent space is structured, for example, based on attributes describing the data to generate. We focus on a particular problem where one aims at generating samples corresponding to a number of objects under various views. We assume that the distribution of the data is driven by two independent latent factors: the content, which represents the intrinsic features of an object, and the view, which stands for the settings of a particular observation of that object. Therefore, we propose a generative model and a conditional variant built on such a disentangled latent space. This approach allows us to generate realistic samples corresponding to various objects in a high variety of views. Unlike many multiview approaches, our model doesn't need any supervision on the views but only on the content. Compared to other conditional generation approaches that are mostly based on binary or categorical attributes, we make no such assumption about the factors of variations. Our model can be used on problems with a huge, potentially infinite, number of categories. We experiment it on four image datasets on which we demonstrate the effectiveness of the model and its ability to generalize.

Review:
The paper proposes a GAN-based method for image generation that attempts to separate latent variables describing fixed "content" of objects from latent variables describing properties of "view" (all dynamic properties such as lighting, viewpoint, accessories, etc). The model is further extended for conditional generation and demonstrated on a range of image benchmark data sets.
The core idea is to train the model on pairs of images corresponding to the same content but varying in views, using adversarial training to discriminate such examples from generated pairs. This is a reasonable procedure and it seems to work well, but also conceptually quite straightforward -- this is quite likely how most people working in the field would solve this problem, standard GAN techniques are used for training the generator and discriminator, and the network architecture is directly borrowed from Radford et al. (2015) and not even explained at all in the paper. The conditional variant is less obvious, requiring two kinds of negative images, and again the proposed approach seems technically sound.
Given the simplicity of the algorithmic choices, the potential novelty of the paper lies more in the problem formulation itself, which considers the question of separating two sets of latent variables from each other in setups where one of them (the "view") can vary from pair to pair in arbitrary manner and no attributes characterising the view are provided. This is an interesting problem setup, but not novel as such and unfortunately the paper does not do a very good job in putting it into the right context. The work is contrasted only against recent GAN-based image generation literature (where covariates for the views are often included) and the aspects related to multi-view learning are described only at the level of general intuition, instead of relating to the existing literature on the topic. The only relevant work cited from this angle is Mathieu et al. (2016), but even that is dismissed lightly by saying it is worse in generative tasks. How about the differences (theoretical and empirical) between the proposed approach and theirs in disentangling the latent variables? One would expect to see more discussion on this, given the importance of this property as motivation for the method.
The generative story using three sets of latent variables, one shared, to describe a pair of objects corresponds to inter-battery factor analysis (IBFA) and is hence very closely related to canonical correlation analysis as well (Tucker "An inter-battery method of factor analysis", Psychometrika, 1958; Klami et al. "Bayesian canonical correlation analysis", JMLR, 2013). Linear CCA naturally would not be sufficient for generative modeling and its non-linear variants (e.g. Wang et al. "Deep variational canonical correlation analysis", arXiv:1610.03454, 2016; Damianou et al. "Manifold relevance determination", ICML, 2012) would not produce visually pleasing generative samples either, but the relationship is so close that these models have even been used for analysing setups identical to yours (e.g. Li et al. "Cross-pose face recognition by canonical correlation analysis", arXiv:1507.08076, 2015) but with goals other than generation. Consequently, the reader would expect to learn something about the relationship between the proposed method and the earlier literature building on the same latent variable formulation. A particularly interesting question would be whether the proposed model actually is a direct GAN-based extension of IBFA, and if not then how does it differ. Use of adversarial training to encourage separation of latent variables is clearly a reasonable idea and quite likely does better job than the earlier solutions (typically based on some sort of group-sparsity assumption in shared-private factorisation) with the possible or even likely exception of Mathieu at al. (2016), and aspects like this should be explicitly discussed to extend the contribution from pure image generation to multi-view literature in general.
The empirical experiments are somewhat non-informative, relying heavily on visual comparisons and only satisfying the minimum requirement of demonstrating that the method does its job. The results look aesthetically more pleasing than the baselines, but the reader does not learn much about how the method actually behaves in practice; when does it break down, how sensitive it is to various choices (network structure, learning algorithm, amount of data,  how well the content and view can be disentangled from each other, etc.). In other words, the evaluation is a bit lazy somewhat in the same sense as the writing and treatment of related work; the authors implemented the model and ran it on a collection of public data sets, but did not venture further into scientific reporting of the merits and limitations of the approach.
Finally, Table 1 seems to have some min/max values the wrong way around.
Revision of the review in light of the author response:
The authors have adequately addressed my main remarks, and while doing so have improved both the positioning of the paper amongst relevant literature and the somewhat limited empirical comparisons. In particular, the authors now discuss alternative multi-view generative models not based on GANs and the revised paper includes considerably extended set of numerical comparisons that better illustrate the advantage over earlier techniques. I have increased my preliminary rating to account for these improvements.