# TiVGAN: Text to Image to Video Generation with Step-by-Step Evolutionary Generator

arXiv: https://arxiv.org/abs/2009.02018 · HTML: https://mlsanity.com/paper/2009.02018/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2020.

## Built on by (most cited first, 19 of 19)

- [Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond](https://mlsanity.com/paper/2405.03520/) (2024)
- [CelebV-Text: A Large-Scale Facial Text-Video Dataset](https://mlsanity.com/paper/2303.14717/) (2023)
- [Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models](https://mlsanity.com/paper/2402.14780/) (2024)
- [Sora as a World Model? A Complete Survey on Text-to-Video Generation](https://mlsanity.com/paper/2403.05131/) (2024)
- [VMBench: A Benchmark for Perception-Aligned Video Motion Generation](https://mlsanity.com/paper/2503.10076/) (2025)
- [Breathing Life Into Sketches Using Text-to-Video Priors](https://mlsanity.com/paper/2311.13608/) (2023)
- [Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning](https://mlsanity.com/paper/2203.02573/) (2022)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text](https://mlsanity.com/paper/2106.14014/) (2021)
- [Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities](https://mlsanity.com/paper/2310.19626/) (2023)
- [Vision + Language Applications: A Survey](https://mlsanity.com/paper/2305.14598/) (2023)
- [Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation](https://mlsanity.com/paper/2210.09549/) (2022)
- [Story Visualization by Online Text Augmentation with Context Memory](https://mlsanity.com/paper/2308.07575/) (2023)
- [Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation](https://mlsanity.com/paper/2209.14046/) (2022)
- [PhysVid: Physics Aware Local Conditioning for Generative Video Models](https://mlsanity.com/paper/2603.26285/) (2026)
- [CoNo: Consistency Noise Injection for Tuning-free Long Video Diffusion](https://mlsanity.com/paper/2406.05082/) (2024)
- [MotionMatcher: Motion Customization of Text-to-Video Diffusion Models via Motion Feature Matching](https://mlsanity.com/paper/2502.13234/) (2025)
- [AI and the creative realm: A short review of current and future applications](https://mlsanity.com/paper/2306.01795/) (2023)
- [AI-based System for Transforming text and sound to Educational Videos](https://mlsanity.com/paper/2601.17022/) (2026)

## Abstract (arXiv)

Advances in technology have led to the development of methods that can create desired visual multimedia. In particular, image generation using deep learning has been extensively studied across diverse fields. In comparison, video generation, especially on conditional inputs, remains a challenging and less explored area. To narrow this gap, we aim to train our model to produce a video corresponding to a given text description. We propose a novel training framework, Text-to-Image-to-Video Generative Adversarial Network (TiVGAN), which evolves frame-by-frame and finally produces a full-length video. In the first phase, we focus on creating a high-quality single video frame while learning the relationship between the text and an image. As the steps proceed, our model is trained gradually on more number of consecutive frames.This step-by-step learning process helps stabilize the training and enables the creation of high-resolution video based on conditional text descriptions. Qualitative and quantitative experimental results on various datasets demonstrate the effectiveness of the proposed method.