Image Generation Models: A Technical History

2026-03-08

AI Summary

This work presents the first comprehensive survey of mainstream image generation techniques developed over the past decade, including variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows, autoregressive models, Transformers, and diffusion models. It systematically traces their evolution in terms of objective functions, architectural designs, and training algorithms, while also extending the discussion to applications such as video generation, deepfake detection, and watermarking. By constructing a clear technological roadmap, the paper synthesizes optimization strategies, failure modes, and inherent limitations across model families, highlighting the progression from static image synthesis to high-quality video generation. Furthermore, it underscores critical ethical and safety considerations regarding model robustness and responsible deployment, offering a systematic reference for future research and practical applications in generative modeling.

Technology Category

Computer Vision: Generative Adversarial Networks (GANs) for VisionMachine Learning: Deep Generative Models & AutoencodersNatural Language Processing: Generation

Application Category

Social Networks and Social Media: Generative AI / large language models and their impact on social systemsSearch and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Image generation has advanced rapidly over the past decade, yet the literature seems fragmented across different models and application domains. This paper aims to offer a comprehensive survey of breakthrough image generation models, including variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows, autoregressive and transformer-based generators, and diffusion-based methods. We provide a detailed technical walkthrough of each model type, including their underlying objectives, architectural building blocks, and algorithmic training steps. For each model type, we present the optimization techniques as well as common failure modes and limitations. We also go over recent developments in video generation and present the research works that made it possible to go from still frames to high quality videos. Lastly, we cover the growing importance of robustness and responsible deployment of these models, including deepfake risks, detection, artifacts, and watermarking.

Problem

• image generation
• literature fragmentation
• model survey
• technical history
• generative models

Innovation

• image generation
• generative models
• diffusion models
• GANs
• responsible AI