# ChatPainter: Improving Text to Image Generation using Dialogue

arXiv: https://arxiv.org/abs/1802.08216 · HTML: https://mlsanity.com/paper/1802.08216/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 18 of 18)

- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Talk-to-Edit: Fine-Grained Facial Editing via Dialog](https://mlsanity.com/paper/2109.04425/) (2021)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Adversarial Text-to-Image Synthesis: A Review](https://mlsanity.com/paper/2101.09983/) (2021)
- [Multimodal Image Synthesis and Editing: The Generative AI Era](https://mlsanity.com/paper/2112.13592/) (2021)
- [3DALL-E: Integrating Text-to-Image AI in 3D Design Workflows](https://mlsanity.com/paper/2210.11603/) (2022)
- [AesopAgent: Agent-driven Evolutionary System on Story-to-Video Production](https://mlsanity.com/paper/2403.07952/) (2024)
- [CHATEDIT: Towards Multi-turn Interactive Facial Image Editing via Dialogue](https://mlsanity.com/paper/2303.11108/) (2023)
- [Vision + Language Applications: A Survey](https://mlsanity.com/paper/2305.14598/) (2023)
- [Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text](https://mlsanity.com/paper/2112.00800/) (2021)
- [Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation](https://mlsanity.com/paper/2209.14046/) (2022)
- [ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis](https://mlsanity.com/paper/2304.06297/) (2023)
- [IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning](https://mlsanity.com/paper/2204.00792/) (2022)
- [BlendScape: Enabling End-User Customization of Video-Conferencing Environments through Generative AI](https://mlsanity.com/paper/2403.13947/) (2024)
- [Fine-Grained Image Generation from Bangla Text Description using Attentional Generative Adversarial Network](https://mlsanity.com/paper/2109.11749/) (2021)
- [Offline Evaluation of Set-Based Text-to-Image Generation](https://mlsanity.com/paper/2410.17331/) (2024)

## Abstract (arXiv)

Synthesizing realistic images from text descriptions on a dataset like Microsoft Common Objects in Context (MS COCO), where each image can contain several objects, is a challenging task. Prior work has used text captions to generate images. However, captions might not be informative enough to capture the entire image and insufficient for the model to be able to understand which objects in the images correspond to which words in the captions. We show that adding a dialogue that further describes the scene leads to significant improvement in the inception score and in the quality of generated images on the MS COCO dataset.