An Investigation of Face Generation Methods Based on Encoders, GAN and Diffusion models

Computer Science, University of British Columbia, V6T1Z4 Vancouver, Canada

^* This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Face generation from natural language input has rapidly emerged as a pivotal research area in computer vision, bridging the gap between creative design and practical commercial applications. This review paper synthesizes findings from ten seminal works to map the methodological evolution of text-to-face generation, tracing the progression from encoder- decoder architectures and Generative Adversarial Networks (GANs) to the current state-of-the-art dominated by diffusion models. This paper examines how these paradigms address core challenges such as generation fidelity, controllability, and cross-modal alignment. The analysis reveals that while early GAN-based approaches pioneered conditional control, modern diffusion models, often integrated with autoencoders, offer superior stability and detail. Furthermore, this paper explores extensions into multi-modal conditioning using audio and pose, as well as applications in synthetic data generation for privacy-preserving facial recognition. Despite significant advances, critical challenges persist, including ensuring precise semantic alignment, maintaining identity across edits, achieving temporal consistency, and mitigating ethical risks associated with deepfakes. This review concludes by identifying key trends and outlining promising future directions for developing more robust, efficient, and ethically sound face generation systems.