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Video Generative Adversarial Networks: A Review

Nuha Aldausari, Arcot Sowmya, Nadine Marcus, Gelareh Mohammadi

arXiv:2011.02250v1cs.CVcs.LGeess.IV

TL;DR

Video GAN research must address the temporal, multimodal, and frame-dependency challenges that distinguish video synthesis from image generation. This paper surveys GAN advances and video models, organizing them into unconditional and condition-specific categories. It concludes that current video GANs remain limited by low frame quality or short videos, while requiring substantial computation and costly data collection.

  • Problem

    Video synthesis is more challenging than image synthesis because videos contain multiple temporally dependent frames and multimodal content such as motion, picture, speed, and soundtrack.

  • Method

    The paper reviews GAN framework advances and systematically categorizes video GANs as unconditional or conditional, further grouping conditional models by their input condition.

  • Results

    Conditional GAN architectures are used in reviewed conditional video models because conditions can enhance network stability and provide higher-quality samples.

  • Takeaways & Limitations

    The survey consolidates video GAN models and their temporal behavior while organizing conditional generation by audio, image, video, text, label, or semantic-map signals.

  • Takeaways & Limitations

    Current video GANs suffer from low-quality frames, few frames, or both, while video generation requires high computational power and complex spatial-temporal architectures.

Abstract

from arXiv · show

With the increasing interest in the content creation field in multiple sectors such as media, education, and entertainment, there is an increasing trend in the papers that uses AI algorithms to generate content such as images, videos, audio, and text. Generative Adversarial Networks (GANs) in 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 among the first survey papers that reviews the state-of-the-art video GANs models. This paper first categorized 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 frameworks that are not initially developed for the video domain but have been adopted in multiple video GANs variations. Then, a comprehensive review of video GANs models is provided under two main divisions according to the presence or non-presence of a condition. The conditional models then further grouped according to the type of condition into audio, text, video, and image. The paper is concluded by highlighting the main challenges and limitations of the current video GANs models. A comprehensive list of datasets, applied loss functions, and evaluation metrics is provided in the supplementary material.

1 INTRODUCTION

GANs generate data resembling training distributions through adversarial generator–discriminator training, but video synthesis adds temporal, multimodal, and frame-dependency challenges. This review addresses the growing literature by extensively surveying video GANs and organizing the paper around GAN advances, related reviews, and video models.

  • GAN foundations: GANs train a generator and discriminator adversarially until generated and real samples become difficult to distinguish.The generator maps random noise to samples, while the discriminator classifies real data as 1 and generated data as 0.
  • GAN foundations: GANs can produce sharper images than VAEs because adversarial training provides a semantic rather than pixel-wise reconstruction signal.The discriminator’s gradients implicitly shape the generator’s reconstruction loss.
  • Video-generation motivation: Video generation is harder than image generation because videos contain multiple temporally dependent frames and multimodal content including motion, picture, speed, and soundtrack.The review identifies temporal coherence and multimodality as central challenges in extending GANs to video.
  • Review scope: The review presents itself as an extensive survey of video GANs following earlier reviews that included only discussions of video models.The paper reports that the first video-GAN attempt appeared in 2016 and that subsequent reviews covered the topic less extensively.
  • Paper organization: The paper covers GAN enhancements, prior reviews, unconditional and conditional video GANs, and concludes with the reviewed models’ differences and limitations.Its sections move from GAN advances and related work to categorized video-generation models and a conclusion.

2 RECENT ADVANCES IN GANS

The paper reviews GAN architectures and improvements adopted in video generation, from the adversarial minimax framework to conditional, convolutional, BigGAN, and Wasserstein variants. These developments address controllability, sample quality, training stability, resolution, and distribution-distance modeling.

  • Vanilla GANs: Vanilla GANs use a generator and discriminator in a minimax game, with the generator producing data from noise and the discriminator separating real from generated samples.Their target is Nash equilibrium, although reaching it is difficult in practice.
  • Conditional GANs: Conditional GANs add a class, text, or other condition to both networks so generated samples are expected to match that condition.The conditional objective modifies the vanilla GAN loss by incorporating the condition.
  • Conditional GANs: InfoGAN controls generated properties by maximizing mutual information between an unknown latent variable and the generated samples.This approach can disentangle features such as color, shape, rotation, or class.
  • Conditional GANs: AC-GAN feeds a condition to the generator while using an auxiliary classifier to predict the input sample’s class rather than feeding the condition to the discriminator.It combines a generator-side condition with auxiliary classification.
  • Video-GAN adoption: Conditional architectures are used in reviewed video-generation models because conditions can improve network stability and sample quality.The passage states this as the reason conditional GAN-based architectures are selected for conditional video generation.
  • Convolutional GANs: Convolutional GANs replace vanilla GAN multilayer perceptrons with deconvolution in the generator and convolutional layers in the discriminator.CNN-based designs are reported to improve performance, sample quality, and training stability relative to MLP-based vanilla GANs.
  • BigGAN and WGAN: BigGAN increases image resolution by scaling parameters and batch size, producing 256×256 and 512×512 ImageNet images.Its architecture is based on self-attention GAN.
  • BigGAN and WGAN: WGAN replaces the vanilla discriminator with a critic that scores the distance between real and generated distributions using Wasserstein distance.This reframes adversarial discrimination as distribution-distance estimation.

3 RELATED WORK

Prior GAN reviews span general frameworks, image synthesis, medical imaging, audio, anomaly detection, and cybersecurity, but the paper identifies a specific gap in systematic video-GAN coverage. It therefore focuses on video models’ temporal behavior and organizes them by conditional setting.

  • Review chronology: The review literature and recent GAN advances are mapped over time using a Google Scholar search and references from retrieved review papers.Figure 3 presents the publication timeline for the surveys and notable GAN advances discussed in the paper.
  • General GAN reviews: General GAN reviews increased between 2019 and 2020, while earlier surveys covered broad concepts, signal-processing analogies, framework improvements, and training disadvantages.One cited comparative study found AC-GANs had the highest MNIST classification accuracy among its evaluated mainstream GANs.
  • Domain-specific reviews: Image-GAN reviews classify synthesis frameworks by architecture, reflecting the image domain’s larger review literature compared with video, text, and voice.The passage attributes this imbalance to GANs’ origins in image-based frameworks.
  • Domain-specific reviews: Medical GAN reviews cover image-quality improvement, augmentation, segmentation, classification, registration, and object detection, including a radiology review of 33 papers.These reviews illustrate task-specific organization within medical imaging.
  • Task-specific reviews: Other specialized reviews address anomaly detection, audio synthesis and enhancement, and cybersecurity uses involving protection or attacks.Audio models use noise or text inputs for generation and noisy audio inputs for enhancement; cybersecurity models synthesize poisoning or adversarial samples.
  • Video-GAN review gap: The paper identifies no systematic review of video GANs despite videos’ temporal dimension and need for temporal cohesiveness.It positions its video review as filling this gap by focusing on models that generate videos and capture temporal behavior.

4 VIDEO GANS

The review organizes video GANs by whether they use conditional signals, then examines unconditional and condition-specific architectures spanning audio, text, semantic maps, images, and videos.

  • Organization: Video GANs are divided into unconditional models and models conditioned on audio, text, semantic maps, images, or videos.The review further groups architectures into RNN-based, progressive, and two-stream designs.
  • Unconditional video generation: Unconditional video GANs generally produce short videos with low-quality frames because training provides no conditional information, but they underpin later conditional frameworks.MoCoGAN is cited as an unconditional model used in Text-Filter.
  • Unconditional video generation: Two-stream VGAN separates moving foreground objects from static backgrounds using 3D spatio-temporal and 2D spatial convolutional networks.The streams are combined before discrimination between real and generated videos.
  • Unconditional video generation: MoCoGAN separates content from movement by decomposing one latent vector per frame into motion and content vectors, whereas TGAN generates each frame from a separate latent vector.TGAN uses N latent vectors for N frames and employs a 3D convolutional discriminator.
  • Conditional video generation: Conditional video synthesis uses signals such as speech audio, encoded text, semantic maps, images, or videos to direct generation and control the output.Examples include synchronized talking faces, progressive text-to-video generation, semantic-map-conditioned synthesis, and image-to-video prediction.
  • Text-to-video synthesis: Progressive text-to-video systems generate an initial image from encoded text, then use that image and text to produce increasingly detailed video frames.The modules increase the number of frames and image quality gradually.
  • Semantic map to video synthesis: Semantic-map-conditioned systems may use a single semantic label map to generate a detailed initial frame before producing the video with a conditional VAE.This approach is described as loosening synthesis restrictions compared with conditioning on a sequence of semantic maps.
  • Image to video synthesis: Early image-to-video systems produced blurry results without disentangled representations, while later methods separated content and motion to improve synthesized-frame quality.Examples use separate encoders or dual GANs for content and dynamics.

5 CONCLUSION

Video GAN research remains relatively early, with quality, duration, computation, data collection, and temporal-spatial modeling still limiting progress. The survey organizes methods by conditioning and highlights alternatives to potentially overfitting 3D convolutions.

  • Challenges and limitations: Current video GANs often suffer from low-quality frames, few frames, or both.The conclusion characterizes video synthesis as less mature than image synthesis.
  • Challenges and limitations: Video generation requires substantial computation because videos are high-dimensional and models must represent spatial and temporal information.The paper notes that DVD-GAN uses one TPU and TGANv2 uses eight GPUs.
  • Challenges and limitations: Video data collection is more time-consuming and expensive than image collection because retrieval remains inaccurate and manual selection, cleaning, and preprocessing are needed.Videos may also include audio, increasing multimodal processing complexity.
  • Architectural choices: Three-dimensional convolutions represent frames and time directly but may cause overfitting, motivating alternatives such as RNNs with 2D convolutions.MoCoGAN is given as an example of the alternative design.
  • Survey contribution: The survey categorizes video GANs as unconditional or conditional, then subdivides conditional methods by audio, image, video, text, label, or semantic-map inputs.It also compiles datasets, evaluation metrics, and loss functions in supplementary material.

A SUPPLEMENTARY MATERIAL

The supplementary material organizes datasets, evaluation measures, and loss functions used across the reviewed video GAN frameworks through coded summary tables.

  • Datasets: Table S1 catalogs datasets used in reviewed video GAN frameworks with coded identifiers, video counts, durations, and scaled resolutions where available.Dataset entries include applications such as robotic manipulation, facial expression, human action, street-scene, and speech-related video.
  • Datasets: The dataset list spans diverse video domains, including human actions, street scenes, dancing, lip reading, facial dynamics, and public-figure speeches.Several entries report unavailable duration or resolution information.
  • Evaluation measures: Table S2 lists evaluation measures used in the reviewed frameworks and links each metric to a coded E identifier.Examples include FID, PSNR, SSIM, LPIPS, WER, segmentation accuracy, and classification accuracy.
  • Loss functions: Table S3 lists loss functions used in reviewed video GAN studies and assigns each loss a coded L identifier for cross-table linking.Listed examples include negative log-likelihood, cosine similarity, feature-matching, semantic layout and pose feature, margin ranking, and forward-backward consistency losses.

A.1 Unconditional GANs

Table S4 summarizes the unconditional video GAN frameworks reviewed in section 4.1 by linking their conditions, losses, datasets, and evaluation measures.

  • Unconditional GANs: Table S4 organizes unconditional video GAN frameworks by condition type, loss functions, training datasets, and evaluation measures.The table directs readers to supplementary tables for additional information.

A.2 Conditional GANs

The supplementary tables classify conditional video GAN frameworks by condition type and connect each reviewed publication with its losses, datasets, and evaluation measures.

  • Speech to video: Table S5 covers speech-to-video GAN frameworks and records their conditional information, losses, datasets, and evaluation measures.It corresponds to the speech-to-video subsection of the conditional review.
  • Text to video: Table S6 covers text-to-video GAN frameworks using the same linked information about conditions, losses, datasets, and evaluation measures.The table points to Tables S1–S3 for expanded dataset, loss, and metric information.
  • Semantic map and image to video: Tables S7 and S8 summarize semantic-map-to-video and image-to-video frameworks, respectively, with their conditions, losses, datasets, and evaluation measures.Both tables link their entries to the supplementary dataset, loss, and evaluation tables.
  • Video to video: Table S9 summarizes video-to-video GAN frameworks and records the same categories of methodological and evaluation information.Its final columns provide losses, datasets, and evaluation measures, with further detail in Tables S1–S3.
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