Generative adversarial training has recently raised significant interest in recommender systems. Adversarial pairwise learning, in particular, has led to methods to select and create unobserved training samples that generalize user preferences, increasing the accuracy and robustness of collaborative filtering (CF) models. Despite this success, only some authors have analyzed adversarial sampling's ability to recommend long-tail and cold-start items, as well as their ability to promote novelty and diversity. These concerns are crucial for modern recommender systems. This paper investigates adversarial pairwise learning in data sparsity scenarios in which most items are consumed by only a few users (long-tail items), and there is a substantial proportion of items without interactions (cold-start items). We found that adversarial sampling increases the bias of CF models toward popular items, resulting in under-recommendation of relevant but less popular long-tail items, poor cold start performance, and low aggregate diversity, i.e., unfair coverage of items among recommendation lists. To address these problems, we propose a multi-modal extension of adversarial pairwise learning that incorporates text and visual information about the items in addition to user-item interaction data. As in the original model, our approach relies on a minimax game. A generative model proposes items for a user considering her visual and textual preferences. Then, a collaborative critic discriminates the suggested items from those already consumed by the user. We conduct experiments on three challenging datasets from the online retail domain in which more than 99.99% of the user-item interactions are unknown, and around 2/3 of the items have less than 5 interactions. We evaluate the advantages of our approach to adversarial and non-adversarial methods, achieving state of the art results in the most complex scenario: the recommendation of new items. Furthermore, we found that the proposed adversarial framework successfully leverages content to make more diverse and novel recommendations. Our code is publicly available on GitHub https://anonymous.4open.science/r/M-APL-D7B7/.