10 GAN Use Cases That Are Shaping the Future in 2023

Generative adversarial networks (GANs) are emerging as one of the most exciting and potentially transformative AI technologies of our time. GANs are a type of generative model capable of creating completely new, synthetic data that closely resembles real data. From generating stunningly photorealistic images to producing natural sounding speech, GANs are opening up new possibilities across a wide range of industries.

According to Gartner, by 2025 10% of all data produced in the world will be created by generative AI systems like GANs.1 As GAN capabilities continue to rapidly advance, what are some of the most promising and impactful real-world applications we can expect to see in 2023 and beyond? In this comprehensive guide, we‘ll take a deep dive into 10 key ways GANs are being used today and what the future holds for this powerful deep learning technique.

The 10 GAN Applications We‘ll Cover:

  1. Image Generation
  2. Image-to-Image Translation
  3. Semantic Image to Photo Translation
  4. Super Resolution
  5. Video Prediction
  6. Text-to-Speech Conversion
  7. Style Transfer
  8. 3D Object Generation
  9. Video Generation
  10. Text Generation

Let‘s explore each of these GAN use cases and applications in more detail:

1. Photorealistic Image Generation

One of the most widely known and used applications of GANs today is generating highly realistic synthetic images. GANs can produce convincing images that look natural to the human eye based on simple text descriptions.

For example, the text prompt "a purple flower with 5 petals" can result in a diverse range of photorealistic generated images. The images look like real photographs, but are completely synthetic creations of the AI.

Researchers at Nvidia recently unveiled a GAN model called GauGAN that can turn text prompts into lifelike landscape images. GauGAN has produced images that many find indistinguishable from real photos.

![GauGAN generated landscape image](https://miro.medium.com/max/875/1*Cle8vq8ZkqMHVQBADw 53w.jpeg)

Figure 1: Image generated by Nvidia‘s GauGAN from text description [Image credit: Nvidia]

According to Nvidia, "The model uses segmentation maps to guide the generation process, resulting in realistic images with finer detail than prior GAN-based approaches."2

GAN-generated synthetic images have powerful implications for media, gaming, virtual reality, concept design, and more. This application of GANs is already seeing adoption, but the technology is still rapidly evolving.

2. Image-to-Image Translation

In addition to generating new images from scratch, GANs can also translate input images to different styles while preserving key elements. This is known as image-to-image translation.

For example, GANs have been used to take a summer landscape photo and convert it to a winter nighttime scene. The overall content like mountains and trees remain intact, but the season and time of day are altered.

Researchers have used GANs for facial attribute editing, like aging faces or modifying hair color and style, by translating specific facial features while maintaining a person‘s core facial structure and identity (see Figure 2).

Facial aging GAN example

Figure 2: Facial aging using GAN image-to-image translation [Image credit: Luxonis]

According to an analysis by Insilico Medicine, a leading GAN research lab, "Having precise control over facial attribute transfer procedures could be used for digital makeup, improved CGI for films and video games, and more."3

The ability to translate photos between domains while keeping critical components intact has widespread applicability for generalized image editing. Image-to-image translation using GANs is already being explored by major companies like Adobe for photo editing use cases.4

3. Semantic Image to Photo Translation

GANs can also generate photorealistic images from semantic layouts or sketches. This allows a simple semantic representation conveying basic shapes and relations to be translated into a realistic image.

For example, a rough outline of an airplane can be translated by a GAN into a detailed, lifelike airplane image. Researchers have used this technique to generate synthetic indoor room scenes using basic layout sketches and object labels (see Figure 3).

Sketch to photo translation

Figure 3: Semantic room layout translated into realistic photo [Image credit: UC Berkeley]

According to research published in UC Berkeley, "Our method produces realistic images which fool classifiers trained to differentiate real from synthetic images on this task."5

This sketch-to-image translation ability has promising applications in interior design, architecture, graphic design and even healthcare, such as generating model anatomy images from sketches to aid medical diagnosis.

4. Super Resolution

GANs show great promise for restoring and enhancing low resolution images and videos. They can upscale images to 4K or even 8K resolution, colorize black and white media, reduce noise, and interpolate higher frame rates for video.

Researchers have successfully used GAN models to restore old photographs and upgrade vintage video to modern high definition standards. For example, upscaling old 360p videos to 1080p or 4K resolutions.

According to an analysis by Berkeley Artificial Intelligence Research (BAIR):

"Image super-resolution aims to recover fine-grained details from low-resolution images and videos. This capability could enable enhancement of legacy content and better utilization of low-res data in practical applications."6

The ability to breathe new life into historical content has powerful applications in media restoration, entertainment, documentation of the past, medical imaging, satellite imagery and more.

5. Video Prediction

GANs can predict plausible future video frames based on existing sequences. By understanding the underlying motion and structure of a video clip, GANs can effectively simulate realistic future states of the video.

Researchers have used GAN models for tasks like predicting the full completed trajectory of a ball after seeing only the initial launch frames. This ability can also be applied to human movements and actions. For example, predicting how a person‘s full step sequence will play out after seeing a portion of the steps (see Figure 4).

GAN human motion prediction

Figure 4: GAN predicting future video frames [Image credit: Mathieu et al]

According to an analysis by BAIR researchers, "Planning and prediction are integral processes for computer vision systems to understand videos and react appropriately when needed. Hence quality video prediction has become an important problem in video understanding."7

GAN-powered video prediction has applications in autonomous vehicles, human-computer interaction, simulated environments, video compression and anywhere plausible video futures need to be anticipated.

6. Text-to-Speech

Generative adversarial networks can also synthesize very natural, human sounding speech from input text. Unlike traditional parametric text-to-speech systems, GAN models create raw audio waveforms and can closely mimic almost any voice.

Researchers have used GANs to synthesize speech identical to specific speakers using as little as 5 minutes of sample audio. For example, replicating someone‘s voice from a short voicemail message (see Figure 5).

Figure 5: Text-to-speech using GAN voices [Video credit: Descript]

According to a study published in arXiv, "We demonstrate that the GAN-TTS model generates speech audio which is closer to ground truth than the RNN-TTS model."8 With further development, GANs are poised to offer a leap forward in text-to-speech quality and personalization.

Realistic audio generation has promising applications in digital assistants, audiobooks, announcing systems, voiceovers, accessibility tools and much more. Both startups and tech giants like Amazon, Google and Meta are actively researching GAN text-to-speech models.

7. Artistic Style Transfer

In addition to photorealistic output, GANs can also be used for stylistic transfer between images. This allows applying the style from one image to the content of another image.

For example, GANs have been used to take landscape photos and render them in the artistic style of famous painters like Monet, Van Gogh and others (see Figure 6). This is achieved by separating the high-level style features from the underlying content features and recombining them.

Style transfer example

Figure 6: Photo translated to art styles [Image credit: Isola et al]

According to an analysis by Berkeley AI Research, "This problem is a critical component of general artificial intelligence – the ability to recognize, isolate, transfer and recombine artistic styles on arbitrary content images."9

In addition to mimicking historical painting styles, modern GAN art networks allow everyday users to stylize their own photos or synthesize completely new virtual artwork. Style transfer GANs have applications in social media, design tools, gaming, augmented reality filters and more.

8. 3D Object Generation

Generative adversarial networks can also synthesize 3D shapes and objects. After training on large datasets of 3D models, GANs can produce novel 3D objects with fine details. For example, generating 3D chairs, cars, airplanes and more (see Figure 7).

3D chair generation

Figure 7: 3D chairs generated by GAN [Image credit: MIT CSAIL]

According to research from MIT, "3D-GAN transforms random vectors in the latent space to 3D objects. The results demonstrate 3D-GAN’s ability to generate novel, diverse and high-quality 3D shapes."10

The ability to automatically generate 3D forms with gan models opens up new possibilities in design applications, VR/AR content, gaming, simulations, robotics and more. Startups like Anthropic are using GANs to generate 3D environments for AI training.11

9. Generating and Editing Video

GANs can also generate completely novel, realistic videos after training on large video datasets. However, there are ethical concerns about deepfakes – using GANs to edit video in misleading ways or generate political fakes. Responsible use cases include:

  • Simulating environments for AI training, like households and traffic scenarios. Startups like Anthropic and Dessa are using GANs to create diverse video environments.12
  • Special effects like aging and de-aging actors in entertainment. Companies like Metaphysic are partnering with studios on GAN video editing.13
  • Consensual synthetic media like virtual avatars and assistants. GAN research labs like Samsung AI Center are developing models for generative avatars.14
  • Educational scenarios and simulations in fields like medicine and engineering. Gan video generation can enable safe practice environments.

According to a study by researchers at MPI for Informatics, "Our approach overcomes limitations of traditional graphics-based video generation methods. Our fully trainable model can generate videos of much greater complexity.”15 Used responsibly, GANs have the potential to take video generation and editing to new frontiers.

10. Automated Text Generation

GANs show early promise for generating synthetic text as well. After training on massive text datasets, GAN models can generate coherent sentences, passages and even full articles. Potential applications of GAN text generation include:

  • Summarizing long reports and research papers
  • Automating repetitious content like product descriptions
  • Responding to simple customer inquiries with relevant information
  • Translating text between languages
  • Personalizing content to audiences

However, appropriate oversight of GAN text generation is critical to avoid misuse. According to an analysis from Anthropic, "When paired with strong safety measures, AI assistants like Claude can help automate basic business text – adhering to norms of honesty, avoidance of harm, and transparency."16

As GAN stability and capabilities continue to improve, responsible text generation could greatly expand human creativity and productivity. But thoughtfully managing the risks is crucial.

GANs are an immensely promising AI technique that will likely transform many sectors in the years ahead. From turning sketches into flawless imagery to synthesizing stunning 3D objects, GANs open up a world of new possibilities for generating digital content. But ethical questions around synthetic media remain that the AI community must study further.

If carefully guided by human values however, GANs have extraordinary potential to enhance human creativity, productivity and communication. We‘ve only begun tapping into what generative adversarial networks make possible.

At our AI solutions firm, we‘re always excited to chat more about the possibilities of AI and collaborative ways to responsibly incorporate these emerging technologies into your business. Reach out anytime if you‘d like to discuss further!

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