Enhanced Face Generation using Generative Adversarial Networks with Feature Augmentation

Abstract

In this work, we propose a lightweight and efficientface generation framework that synthesizes realistic human facial images from semantic attribute inputs using Transformer models integrated with StyleGAN2-T. The system takes high-level descriptors such as gender, age, hair colour, eye colour, face shape, hair type, and ethnicity as input, which are processed using Transformer-based encoders to capture contextualrelationships and feature dependencies among the attributes. These embeddings are then translated into the latent space of the StyleGAN2-T generator, enabling high-quality facial image synthesis with reduced computational cost and faster inference time. StyleGAN2-T, a distilled variant of StyleGAN2, is employed to maintain image realism while ensuring responsiveness, making the model suitable for real-time applications. The combination of language-based understanding and generative modelling offers a novel pipelinethat bridges human-descriptive semantics and machine-driven image synthesis. Experimental results demonstrate the system\'s ability to generate visually coherent faces across diverse attributecombinations, with potential use cases in digital avatar creation, gaming, virtual reality, and identity reconstruction.

Introduction

Overview:
The fusion of computer vision and generative modeling has enabled realistic face image synthesis. Traditional CNN-based GANs (e.g., StyleGAN) lack semantic control and struggle with long-range dependencies. To address these issues, a hybrid system combining Transformer-based models (like CLIP) with StyleGAN2-T is proposed, enabling controllable and realistic face generation from textual attributes (e.g., age, gender, hair type).

Key Components:

1. User Input & Semantic Prompting:

   • Users specify facial features via a web interface (e.g., age, ethnicity, hair type).

   • Inputs are converted into natural language prompts (e.g., “a 30-year-old African man…”).

2. Text-to-Latent Embedding (CLIP):

   • The prompt is encoded using a Transformer model (CLIP) to generate a dense latent vector aligned with facial features.

3. Image Generation (StyleGAN2-T):

   • The latent vector is fed into StyleGAN2-T, a lightweight GAN optimized for speed and realism.

   • Realistic facial images are generated with high fidelity and low computational cost.

4. Web Interface (Flask):

   • The application is deployed via Flask, enabling users to view, download, or save the generated face images.

System Architecture & Advantages:

• Combines semantic richness of Transformers with efficiency of StyleGAN2-T.

• Enables fine-grained control over facial features.

• Modular design allows for model upgrades (e.g., swap StyleGAN2-T with Stable Diffusion).

• Supports real-time, scalable deployment on cloud or edge devices.

Performance Analysis:

• The hybrid model demonstrates superior accuracy, realism, and attribute consistency.

Conclusion

This work presents an innovative and efficient approach to human face generation by integrating Transformer-based attribute encoding with the StyleGAN2-T generator. By translating semantic descriptors such as age, gender, hair colour, and ethnicity into high-quality facial images, the system successfully bridges the gap between textual input and visual synthesis. The use of StyleGAN2-T enhances both the visual fidelity and computational efficiency, making the solution practical for real-time applications such as avatar creation and virtual reality environments. Overall, the proposed framework demonstrates strong potential for personalized face generation with minimal latency, highlighting the effectiveness of combining language-based models with generative adversarial networks.

References

[1] StyleGAN2 (Official Paper)Title: Analysing and Improving the Image Quality of StyleGANAuthors: Tero Karras, Samuli Laine, Timo AilaLink: https://doi.org/10.48550/arXiv.1912.04958 [2] StyleFlow (for controlled attribute manipulation with StyleGAN2)Title: Style Flow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing FlowsAuthors: Rameen Abdal, Peihao Zhu, Peter WonkaLink: https://doi.org/10.1145/3447648 [3] Face Aging using GANs (Feature Augmentation Example)Title: Face Aging With Conditional Generative Adversarial NetworksAuthors: Grigory Antipov, Moez Baccouche, Jean-Luc DugelayLink: https://doi.org/10.48550/arXiv.1702.01983 [4] Semantic Face Editing in StyleGAN Latent SpaceTitle: Interpreting the Latent Space of GANs for Semantic Face EditingAuthors: Yujun Shen, Jinjin Gu, Xiaoou Tang, Bolei ZhouLink: https://doi.org/10.48550/arXiv.1907.10786

Copyright

Copyright © 2025 Mrs. B. Haritha, S. Naga Jyothi, S. Mani Meghana, S. Naga Sri, N. Meghana. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.