# A Survey of AI Text-to-Image and AI Text-to-Video Generators

arXiv: https://arxiv.org/abs/2311.06329 · HTML: https://mlsanity.com/paper/2311.06329/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://mlsanity.com/paper/2105.13290/) (2021)
- [Muse: Text-To-Image Generation via Masked Generative Transformers](https://mlsanity.com/paper/2301.00704/) (2023)
- [Phenaki: Variable Length Video Generation From Open Domain Textual Description](https://mlsanity.com/paper/2210.02399/) (2022)
- [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021)
- [CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers](https://mlsanity.com/paper/2204.14217/) (2022)
- [GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions](https://mlsanity.com/paper/2104.14806/) (2021)
- [N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtion](https://mlsanity.com/paper/2111.12417/) (2021)
- [Identifying and Reducing Gender Bias in Word-Level Language Models](https://mlsanity.com/paper/1904.03035/) (2019)
- [Understanding and Creating Art with AI: Review and Outlook](https://mlsanity.com/paper/2102.09109/) (2021)
- [Text-to-Image Generation with Attention Based Recurrent Neural Networks](https://mlsanity.com/paper/2001.06658/) (2020)

## Built on by (most cited first, 17 of 17)

- [Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG](https://mlsanity.com/paper/2501.09136/) (2025)
- [Sora as a World Model? A Complete Survey on Text-to-Video Generation](https://mlsanity.com/paper/2403.05131/) (2024)
- [A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming](https://mlsanity.com/paper/2404.16038/) (2024)
- [A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges](https://mlsanity.com/paper/2412.04782/) (2024)
- [Simulating the Real World: A Unified Survey of Multimodal Generative Models](https://mlsanity.com/paper/2503.04641/) (2025)
- [LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation](https://mlsanity.com/paper/2505.12098/) (2025)
- [Exploring Prompt Engineering: A Systematic Review with SWOT Analysis](https://mlsanity.com/paper/2410.12843/) (2024)
- [A Survey: Spatiotemporal Consistency in Video Generation](https://mlsanity.com/paper/2502.17863/) (2025)
- [Bridging Text and Video Generation: A Survey](https://mlsanity.com/paper/2510.04999/) (2025)
- [Compliance Rating Scheme: A Data Provenance Framework for Generative AI Datasets](https://mlsanity.com/paper/2512.21775/) (2025)
- [Foundations of GenIR](https://mlsanity.com/paper/2501.02842/) (2025)
- [SSG-Dit: A Spatial Signal Guided Framework for Controllable Video Generation](https://mlsanity.com/paper/2508.17062/) (2025)
- [Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?](https://mlsanity.com/paper/2509.18461/) (2025)
- [Process-of-Thought Reasoning for Videos](https://mlsanity.com/paper/2602.07689/) (2026)
- [SKeDA: A Generative Watermarking Framework for Text-to-video Diffusion Models](https://mlsanity.com/paper/2603.00194/) (2026)
- [Are Video Generation Models Geographically Fair? An Attraction-Centric Evaluation of Global Visual Knowledge](https://mlsanity.com/paper/2601.18698/) (2026)
- [Polaris: Scaling Up Instruction-Guided Image Generation Towards Millions of Personalized Style Needs](https://mlsanity.com/paper/2606.01858/) (2026)

## Abstract (arXiv)

Text-to-Image and Text-to-Video AI generation models are revolutionary technologies that use deep learning and natural language processing (NLP) techniques to create images and videos from textual descriptions. This paper investigates cutting-edge approaches in the discipline of Text-to-Image and Text-to-Video AI generations. The survey provides an overview of the existing literature as well as an analysis of the approaches used in various studies. It covers data preprocessing techniques, neural network types, and evaluation metrics used in the field. In addition, the paper discusses the challenges and limitations of Text-to-Image and Text-to-Video AI generations, as well as future research directions. Overall, these models have promising potential for a wide range of applications such as video production, content creation, and digital marketing.