FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On

With the increasing interest in the content creation field in multiple sectors such as media, education, and entertainment, there is an increased trend in the papers that use AI algorithms to generate content such as images, videos, audio, and text. Generative Adversarial Networks (GANs) is one of the promising models that synthesizes data samples that are similar to real data samples. While the variations of GANs models in general have been covered to some extent in several survey papers, to the best of our knowledge, this is the first paper that reviews the state-of-the-art video GANs models. This paper first categorizes GANs review papers into general GANs review papers, image GANs review papers, and special field GANs review papers such as anomaly detection, medical imaging, or cybersecurity. The paper then summarizes the main improvements in GANs that are not necessarily applied in the video domain in the first run but have been adopted in multiple video GANs variations. Then, a comprehensive review of video GANs models are provided under two main divisions based on existence of a condition. The conditional models are then further classified according to the provided condition into audio, text, video, and image. The paper concludes with the main challenges and limitations of the current video GANs models.

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“…Cai et al [69] input image, label Generating, predicting, and completing frames. FW-GAN [76] two images, motion map, previous frames…”

Section: Publicationmentioning

confidence: 99%

“…The dominant application of image-to-video GAN frameworks is video prediction applications, yet there are other applications such as video colorization [75] and video virtual try-on [76]. Zhang et al [75] introduce GANs in exemplar-based colorization problems.…”

Section: Publicationmentioning

confidence: 99%

Video Generative Adversarial Networks: A Review

et al. 2022

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“…Han et al [22] presented instead a model for pose-guided image generation and virtual try-on that estimates a dense flow between source and target clothing region. Differently, Dong et al [23] went beyond image-based virtual try-on and proposed a video-based solution that learns to synthesize a video of try-on results based on a person image, a try-on garment, and a series of target body poses.…”

Section: Related Workmentioning

confidence: 99%

VITON-GT: An Image-based Virtual Try-On Model with Geometric Transformations

Fincato

Landi

Cornia

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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The large spread of online shopping has led computer vision researchers to develop different solutions for the fashion domain to potentially increase the online user experience and improve the efficiency of preparing fashion catalogs. Among them, image-based virtual try-on has recently attracted a lot of attention resulting in several architectures that can generate a new image of a person wearing an input try-on garment in a plausible and realistic way. In this paper, we present VITON-GT, a new model for virtual try-on that generates high-quality and photo-realistic images thanks to multiple geometric transformations. In particular, our model is composed of a two-stage geometric transformation module that performs two different projections on the input garment, and a transformation-guided try-on module that synthesizes the new image. We experimentally validate the proposed solution on the most common dataset for this task, containing mainly t-shirts, and we demonstrate its effectiveness compared to different baselines and previous methods. Additionally, we assess the generalization capabilities of our model on a new set of fashion items composed of upper-body clothes from different categories. To the best of our knowledge, we are the first to test virtual try-on architectures in this challenging experimental setting.

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“…The work of Han et al [91] focus on transferring the appearance naturally and synthesizing novel result by proposing ClothFlow model. In addition to their approaches related to image-based virtual try-on, Dong et al [92] presented a Flow-Navigated Warping GAN for Video (FW-GAN) which aimed to synthesize a video of virtual try-on results.…”

Section: Style Transfermentioning

confidence: 99%

You can Try without Visiting: A Comprehensive Survey on Virtually Try-on Outfits

Ghodhbani¹,

Alimi²,

Néji³

et al. 2021

Preprint

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<p>Our work aims to conduct a comprehensive literature review of deep learning methods applied in the fashion industry and, especially, the image-based virtual fitting task by citing research works published in the last years. We have summarized their challenges, their main frameworks, the popular benchmark datasets, and the different evaluation metrics. Also, some promising future research directions are discussed to propose improvements in this research field.</p>

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FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On

Cited by 99 publications

References 32 publications

Video Generative Adversarial Networks: A Review

Video Generative Adversarial Networks: A Review

VITON-GT: An Image-based Virtual Try-On Model with Geometric Transformations

You can Try without Visiting: A Comprehensive Survey on Virtually Try-on Outfits

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