# Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language

arXiv: https://arxiv.org/abs/1810.11919 · HTML: https://mlsanity.com/paper/1810.11919/

## Key facts

- Cited by 90 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2018.

## Built on by (most cited first, 30 of 90)

- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [Text2LIVE: Text-Driven Layered Image and Video Editing](https://mlsanity.com/paper/2204.02491/) (2022)
- [VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance](https://mlsanity.com/paper/2204.08583/) (2022)
- [More Control for Free! Image Synthesis with Semantic Diffusion Guidance](https://mlsanity.com/paper/2112.05744/) (2021)
- [Guiding Instruction-based Image Editing via Multimodal Large Language Models](https://mlsanity.com/paper/2309.17102/) (2023)
- [Talk-to-Edit: Fine-Grained Facial Editing via Dialog](https://mlsanity.com/paper/2109.04425/) (2021)
- [EnvEdit: Environment Editing for Vision-and-Language Navigation](https://mlsanity.com/paper/2203.15685/) (2022)
- [Shape-aware Text-driven Layered Video Editing](https://mlsanity.com/paper/2301.13173/) (2023)
- [Adversarial Text-to-Image Synthesis: A Review](https://mlsanity.com/paper/2101.09983/) (2021)
- [Improving Generation and Evaluation of Visual Stories via Semantic Consistency](https://mlsanity.com/paper/2105.10026/) (2021)
- [StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation](https://mlsanity.com/paper/2112.08493/) (2021)
- [Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion](https://mlsanity.com/paper/2303.08767/) (2023)
- [Language-Driven Image Style Transfer](https://mlsanity.com/paper/2106.00178/) (2021)
- [M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers](https://mlsanity.com/paper/2105.14211/) (2021)
- [Sound-Guided Semantic Image Manipulation](https://mlsanity.com/paper/2112.00007/) (2021)
- [Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model](https://mlsanity.com/paper/2111.13333/) (2021)
- [AnyFace: Free-style Text-to-Face Synthesis and Manipulation](https://mlsanity.com/paper/2203.15334/) (2022)
- [InstructX: Towards Unified Visual Editing with MLLM Guidance](https://mlsanity.com/paper/2510.08485/) (2025)
- [DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation](https://mlsanity.com/paper/2303.06285/) (2023)
- [DeFLOCNet: Deep Image Editing via Flexible Low-level Controls](https://mlsanity.com/paper/2103.12723/) (2021)
- [Towards Open-World Text-Guided Face Image Generation and Manipulation](https://mlsanity.com/paper/2104.08910/) (2021)
- [Learning by Planning: Language-Guided Global Image Editing](https://mlsanity.com/paper/2106.13156/) (2021)
- [Region-Aware Diffusion for Zero-shot Text-driven Image Editing](https://mlsanity.com/paper/2302.11797/) (2023)
- [A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU](https://mlsanity.com/paper/2305.17473/) (2023)
- [One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations](https://mlsanity.com/paper/2210.07883/) (2022)
- [Word-Level Fine-Grained Story Visualization](https://mlsanity.com/paper/2208.02341/) (2022)
- [Learning Visual Styles from Audio-Visual Associations](https://mlsanity.com/paper/2205.05072/) (2022)
- [KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing](https://mlsanity.com/paper/2309.16608/) (2023)
- [Embedding Arithmetic of Multimodal Queries for Image Retrieval](https://mlsanity.com/paper/2112.03162/) (2021)

## Abstract (arXiv)

This paper addresses the problem of manipulating images using natural language description. Our task aims to semantically modify visual attributes of an object in an image according to the text describing the new visual appearance. Although existing methods synthesize images having new attributes, they do not fully preserve text-irrelevant contents of the original image. In this paper, we propose the text-adaptive generative adversarial network (TAGAN) to generate semantically manipulated images while preserving text-irrelevant contents. The key to our method is the text-adaptive discriminator that creates word-level local discriminators according to input text to classify fine-grained attributes independently. With this discriminator, the generator learns to generate images where only regions that correspond to the given text are modified. Experimental results show that our method outperforms existing methods on CUB and Oxford-102 datasets, and our results were mostly preferred on a user study. Extensive analysis shows that our method is able to effectively disentangle visual attributes and produce pleasing outputs.