Metya AI Technology Framework

Metya is an AI-driven Web3 social platform, dedicated to creating a smarter, more personalized user experience through multilingual support, content moderation, and multimodal interaction capabilities such as text, voice, image, and video. This technical solution aims to enhance the depth of user social interactions while enabling AI-powered data analysis and emotion recognition based on user behavior.

1. Technical Architecture Overview:

Metya’s core AI technical architecture is designed with a multi-layered, multi-module system structure to support the platform's rich social interaction and data processing requirements.

The core components of its architecture include:

• Multimodal Data Processing Module:Metya's multimodal data processing module can simultaneously handle text, voice, image, and video inputs, supporting various interaction methods ranging from text chat to video calls. By leveraging Natural Language Processing (NLP) technology, the system can accurately analyze and understand the content of user inputs. At the same time, Computer Vision (CV) technology processes uploaded images and videos, achieving unified data analysis and feedback across different media.

• Decentralized Data Storage and Smart Contracts: Metya uses blockchain technology to store and manage user behavior data. All social interaction data is processed through smart contracts and directly recorded on a distributed ledger, ensuring data transparency and immutability. This decentralized storage method not only protects user data privacy but also provides a reliable reward mechanism, automatically distributing rewards through smart contracts.

• Real-Time Emotion Recognition and Personalized Recommendation Engine: AI algorithms on the Metya platform can capture users’ emotional states and interaction preferences in real time through machine learning models. This real-time emotion recognition technology allows the system to adjust conversation content, recommend appropriate interaction methods, and generate content that aligns with user needs. Whether it's a text chat or a video call, the system ensures the authenticity of user experience and the precision of emotional feedback.

Metya’s technical architecture is a comprehensive system built by integrating multiple advanced technologies. It flexibly adapts to various user needs and social scenarios. By deeply integrating blockchain and AI technologies, Metya creates an efficient and transparent social network infrastructure, enabling users to interact and create value in a secure, open environment.

2.Technical Applications

Metya's AI technology is widely applied across various social platform scenarios, significantly enhancing user engagement and interaction experience on the platform. Here are the primary application areas of the technology:

Personalized Chat Assistant and Emotional Interaction:

Metya utilizes advanced AI algorithms to provide users with personalized chat experiences. The system can analyze users' emotional states in real time during conversations and automatically generate appropriate reply suggestions based on context. This personalized interaction boosts social efficiency and increases user engagement, making each chat feel more natural and enjoyable. The AI-powered emotional assistant can also adjust tone and content according to user preferences, creating a more thoughtful communication experience.

Virtual Human Creation and Dynamic Simulation:

Through AI technology, Metya creates virtual human models that can interact dynamically with users in various forms. These virtual humans can communicate with users through voice, video, and other methods and adjust themselves based on user feedback and emotional states. These AI-supported virtual humans are not just simple role setups but intelligent entities with emotional responses and personalized traits, providing ongoing emotional companionship and social support for users.

Intelligent Multilingual Support and Cross-Cultural Interaction：

Metya's AI system supports real-time translation and natural language processing in 138 global languages, enabling seamless communication for users in different linguistic environments. The multilingual support function not only breaks down language barriers but also captures the emotional tones and expressions of users during cross-cultural exchanges. Whether chatting with friends from different countries or participating in multilingual social events, Metya's multilingual features offer a smooth experience, providing a truly globalized social interaction.

Virtual Girlfriend/Boyfriend Integration：

1. Virtual Human Creation, Personalized virtual characters are created based on the user's data, appearing as platform users.

2. Multimodal Chat, Depending on the real-life scenario, the virtual human can respond via text, voice, images, and video in multiple languages, including but not limited to English, Simplified Chinese, Traditional Chinese, Japanese, Korean, Thai, and Russian.

3. Memory and Emotion Recognition

4. Support for Open-ended Conversations

5. Image-to-Image Generation: Generate selfies and personal images in various scenarios based on the user's facial features.

3.Metya AI Image Computation Demonstration

Metya's AI image generation technology is based on deep learning and generative adversarial networks (GANs), integrating natural language processing (NLP) and computer vision (CV) algorithms to deliver intelligent image generation capabilities:

Image Generation Model:

This feature can automatically generate selfies and personal pictures in different situations based on the user's facial features, meeting the diverse creative and expressive needs of users.

Through natural language processing technology, Metya AI can understand the descriptions input by users and, through the deep integration of NLP and CV, transform the user's intentions into specific image details. The system generates personalized selfies and photos in various scenarios based on the user's facial information, ensuring that the generated content not only meets the user's emotional needs but also has a high degree of realism and artistic expressiveness.

The Metya AI image model is similar to VAE, aiming to project raw data into latent space and recover it from latent space to raw data. The core of this method is to simulate a series of Gaussian noise distributions in a Markov chain, gradually adding Gaussian noise to the original signal (such as images, audio) to generate a signal that follows a Gaussian distribution. Then, train a learning model to gradually restore to .

Image Creation Process:

The computational process consists of two primary steps:

1. Diffusion Process: The process of gradually adding Gaussian noise to the signal ( → … → → → … →). In this process, q(x) is not trainable.

2. Reverse Process: The process of gradually restoring the signal from the noise → … → → → … →). In this process, p(x) is trainable, and the desired learning model can be obtained after training is completed.

The diffusion process adopts a fixed form of Markov chain, meaning that the current state is completely unrelated to past states. By gradually adding Gaussian noise to the image, given the initial data distribution ~ , Gaussian noise can be continuously added to the distribution. The variance of this noise is determined by , with the mean being determined by and the at the current moment . As the noise is continuously added, the final generated data is produced.

The process of gradually increasing noise

The reverse diffusion process aims to recover the original data from Gaussian noise. It remains a Markov chain, and we can assume it is also a Gaussian distribution. Therefore, the goal of the model p is to learn the mean and variance of this Gaussian distribution, and then gradually restore XT to X0 through consecutive multiplication.

Slowly reversing the diffusion until the image becomes clear

During the model training process, gradient descent is applied to the loss. After a series of derivations and simplifications, the loss can ultimately be reduced to

Where represents Gaussian noise.. The training objective is to ensure that the predicted noise matches the actual noise, which means calculating the L2 loss between the two noises.

For sampling, the noisy image is input into the model, and then the image is continuously refined to become clearer.

Through mathematical derivation, the mean can be obtained.

Eventually, the generative model for the previous moment can be obtained.

The core of this feature lies in using a diffusion model for image generation. Through the diffusion process, the system gradually adds Gaussian noise to the original image to form a noisy image; subsequently, the reverse diffusion process uses a trained model to gradually restore the noisy image to a clear original image. This process ensures that the generated images achieve a high level of quality and realism.

Metya AI's image generation not only focuses on technical implementation but also emphasizes user experience, ensuring that the generated content meets the user's emotional needs and provides a realistic and artistic visual effect. Users can interact with the system through natural language, enjoying a relaxed and pleasant creative process.