Generative Adversarial Networks (GANs) are a deep learning architecture used for generative tasks, such as image generation, style transfer, and text-to-image synthesis. They consist of two neural networks: a generator and a discriminator. The generator creates new data samples while the discriminator evaluates them, trying to distinguish real data from generated ones. During training, the two networks play a two-player minimax game, where the generator tries to fool the discriminator, and the discriminator tries to classify real data from generated data correctly. The competition between the two networks eventually leads to the generator producing realistic synthetic data samples. In recent years, there has been a significant advancement in GANs for image generation, particularly in sign language production. This article provides a comprehensive overview of the latest developments in GANs and how they are being applied to the field of sign language production.
Sign Language Recognition (SLR) and Sign Language Production (SLP)
The World Health Organization estimates that 466 million individuals around the globe are either completely deaf or have severe hearing loss. Developing reliable systems capable of translating spoken languages into sign languages and vice versa is crucial for easing and improving communication between the hearing and the Deaf. Sign Language Recognition (SLR) and Sign Language Production (SLP) can aid in this two-way communication (SLP).
Fig 1: Sign Language Production and Recognition
Contrary to spoken languages, which use a single synchronous channel (called an articulator in linguistics), sign languages use a variety of asynchronous channels to express meaning. Both manual (such as head and shoulder movement) and non-manual (such as facial expressions, mouthing, and body posture) characteristics are sent through these channels.
There is a new strategy for advancing SLP that draws on techniques from natural language processing, computer graphics, and neural network–based picture and video production. We can use the proposed method to produce a sign language video if we have a written or spoken message. An encoder-decoder network generates a series of gloss probabilities from the text inputted in the target language to identify a pose sequence that accurately represents the input. Finally, the sequence is fed into a GAN as training data to generate a video of sign language interpretations of the input message. In a nutshell, this work makes the following contributions:
- Continuous text-to-pose translation was made possible using a network based on neural machine translation (NMT) and a motion graph.
- Pose and appearance data feed into a generative network.
Neural Machine Translation
Neural machine translation uses deep neural networks to learn the mapping between the source language (e.g. spoken English) and target language (e.g. American Sign Language). The system is trained on large datasets to capture the patterns and relationships between the two languages and then generates a sign language representation of the spoken language input. This method uses neural networks to analyze and understand the meaning of the written text and then generate the corresponding signs in real-time.
One of the major advantages of this method is its ability to handle large amounts of data, making it possible to translate complex sentences into sign language. The use of deep learning algorithms enables the system to learn the complex relationships between the written text and the corresponding signs, leading to improved accuracy and speed of translation.
Avatar Approaches for Sign Language Production
Avatar-based sign language production involves the creation of an avatar that can be controlled to generate signs. The avatar is designed using a 3D model and an animation system, which allows the generation of signs from a given set of parameters. This approach is based on deep learning algorithms that can capture a sign’s essential features, such as the hand’s shape and motion.
To train the avatar, a large dataset of sign language is required. The data is used to train the deep-learning algorithms that control the avatar, allowing it to generate realistic signs. This approach has been found to be effective in generating highly realistic signs with a high degree of accuracy.
A motion graph is a graph-based model that represents the motion of a sign language gesture. The graph comprises nodes representing key frames of the gesture and edges representing the transitions between the frames. The nodes are obtained through image processing techniques, such as contour detection and keypoint detection, while the edges are obtained through the computation of motion vectors. The motion graph represents the motion of the sign language gesture in a compact and interpretable form.
The use of motion graphs in sign language production has several advantages. Firstly, the motion graph allows for the generation of continuous sign language gestures, as the transitions between the frames are explicitly represented. This is in contrast to traditional GANs, which typically generate discrete gestures. Secondly, the motion graph allows for the representation of sign language gestures in a compact form, which reduces the amount of data needed to be stored and processed. This is particularly useful for language production, as sign language gestures can be very complex and require large amounts of data to be stored and processed.
Finally, the motion graph allows for the manipulation of sign language gestures, as the graph can be edited to change the motion of the gesture. This is useful for sign language production, as it allows for the creation of new sign language gestures or the modification of existing gestures in a flexible and intuitive way.
Conditional Image Generation
Conditional image generation is a technique that is used to generate images that are conditioned on a given input. In the context of sign language production, this technique has been applied to generate images of sign language gestures that are conditioned on the meaning of the gesture.
The conditional image generation process involves using a GAN architecture that consists of two neural networks: a generator network and a discriminator network. The generator network generates images of sign language gestures, while the discriminator network evaluates the generated images to determine whether they are realistic. The two networks are trained in an adversarial manner, where the generator tries to generate images that are as realistic as possible. In contrast, the discriminator tries to determine whether the generated images are real.
The conditional image generation process is conditioned on the meaning of the sign language gesture, which is represented as a one-hot vector. The one-hot vector represents the category of the gesture, such as the letter A or the number 1, and is fed into the generator network as an input. The generator network then generates an image of the sign language gesture based on the meaning represented by the one-hot vector.
The use of conditional image generation in sign language production has several advantages. Firstly, it allows for the generation of images of sign language gestures that are conditioned on the meaning of the gesture, which is a significant step towards the production of sign language for individuals who are deaf or hard of hearing. Secondly, using GANs in conditional image generation provides a powerful solution for generating sign language images.
Text-to-Sign Language Translation
Text-to-sign language translation is the process of converting written or spoken language into sign language. This process is essential for people with hearing disabilities who communicate through sign language. The world’s most widely used sign languages include American Sign Language (ASL) and British Sign Language (BSL). The goal of text-to-sign language translation is to provide a seamless communication experience for people with hearing disabilities who are unable to access audio information.
Fig 2: Text-to-Sign Language Translation
Text to Pose Translation
Text to Pose Translation is a technique that involves mapping an input text description of a human body posture into a corresponding target pose image. The main aim of this technique is to generate realistic images of human poses based on textual descriptions.
The process of Text to Pose Translation is typically done using Generative Adversarial Networks (GANs). The architecture of the GANs consists of two main components: the Image Generator and the Discriminator.
The Image Generator takes in the input text description and generates a corresponding pose image. The Discriminator then evaluates the generated image and determines whether it is a realistic pose. The Image Generator and the Discriminator work together in a game-theoretic fashion. The Image Generator aims to generate realistic images, and the Discriminator aims to detect whether the images are real or fake.
Pose to Video Translation
The development of generative adversarial networks (GANs) has allowed for great advances in generating images from scratch. This has led to new applications in image generation, such as creating poses from videos or translating videos into different formats.
One recent example is the work by Song et al., who used a GAN to create an end-to-end system for translating American Sign Language (ASL) gestures into corresponding video sequences. The system consists of two main components: a pose generator and a video generator.
The pose generator inputs a sequence of ASL gestures and outputs a corresponding sequence of poses. The video generator then takes this pose sequence and creates a video that shows the signer performing the gestures.
The system was trained on a dataset of over 2000 ASL videos and can generate realistic videos of signing fingerspelled words, numbers, and sentences. This is a significant advance over previous methods, which required manual alignment of individual frames or relied on synthesized speech to recreate signing videos.
This research opens up new possibilities for the automatic translation of signed languages into text or other spoken languages. It also has potential applications in virtual reality, where realistic signing avatars could be generated from user input.
The advancement in Generative Adversarial Networks for image generation has revolutionized the field of computer vision and machine learning. With the ability to generate high-quality images, GANs have found various applications in diverse domains, such as medical imaging, gaming, and sign language production. The combination of deep learning and adversarial training allows GANs to generate indistinguishable images from authentic images, making it a powerful tool for sign language production. The future of GANs for image generation looks promising as researchers continue to explore new ways to improve the quality and accuracy of generated images. With ongoing advancements in hardware and software, GANs are likely to play a crucial role in bridging the gap between sign language and the hearing world, providing a platform for effective communication between both communities.