# Semantics Disentangling for Text-to-Image Generation

arXiv: https://arxiv.org/abs/1904.01480 · HTML: https://mlsanity.com/paper/1904.01480/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 30 of 61)

- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)
- [Cross-Modal Contrastive Learning for Text-to-Image Generation](https://mlsanity.com/paper/2101.04702/) (2021)
- [Paint by Word](https://mlsanity.com/paper/2103.10951/) (2021)
- [GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis](https://mlsanity.com/paper/2301.12959/) (2023)
- [Make-A-Story: Visual Memory Conditioned Consistent Story Generation](https://mlsanity.com/paper/2211.13319/) (2022)
- [StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation](https://mlsanity.com/paper/2209.06192/) (2022)
- [Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation](https://mlsanity.com/paper/2304.01816/) (2023)
- [Improving Text-to-Image Synthesis Using Contrastive Learning](https://mlsanity.com/paper/2107.02423/) (2021)
- [CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP](https://mlsanity.com/paper/2203.00386/) (2022)
- [Text to Image Generation with Semantic-Spatial Aware GAN](https://mlsanity.com/paper/2104.00567/) (2021)
- [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)
- [RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model](https://mlsanity.com/paper/2309.00810/) (2023)
- [Prompting Hard or Hardly Prompting: Prompt Inversion for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2312.12416/) (2023)
- [StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis](https://mlsanity.com/paper/2203.15799/) (2022)
- [High-Fidelity 3D Face Generation from Natural Language Descriptions](https://mlsanity.com/paper/2305.03302/) (2023)
- [Cycle-Consistent Inverse GAN for Text-to-Image Synthesis](https://mlsanity.com/paper/2108.01361/) (2021)
- [GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation](https://mlsanity.com/paper/2303.10056/) (2023)
- [Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty](https://mlsanity.com/paper/2412.06771/) (2024)
- [Attribute-Centric Compositional Text-to-Image Generation](https://mlsanity.com/paper/2301.01413/) (2023)
- [FEAT: Face Editing with Attention](https://mlsanity.com/paper/2202.02713/) (2022)
- [CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization](https://mlsanity.com/paper/2311.14631/) (2023)
- [TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation](https://mlsanity.com/paper/2112.01398/) (2021)
- [DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation](https://mlsanity.com/paper/2209.01339/) (2022)
- [PartCraft: Crafting Creative Objects by Parts](https://mlsanity.com/paper/2407.04604/) (2024)
- [CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation](https://mlsanity.com/paper/2107.13516/) (2021)
- [DR-GAN: Distribution Regularization for Text-to-Image Generation](https://mlsanity.com/paper/2204.07945/) (2022)
- [Layout-Bridging Text-to-Image Synthesis](https://mlsanity.com/paper/2208.06162/) (2022)
- [Deep Image Synthesis from Intuitive User Input: A Review and Perspectives](https://mlsanity.com/paper/2107.04240/) (2021)
- [Vision + Language Applications: A Survey](https://mlsanity.com/paper/2305.14598/) (2023)

## Abstract (arXiv)

Synthesizing photo-realistic images from text descriptions is a challenging problem. Previous studies have shown remarkable progresses on visual quality of the generated images. In this paper, we consider semantics from the input text descriptions in helping render photo-realistic images. However, diverse linguistic expressions pose challenges in extracting consistent semantics even they depict the same thing. To this end, we propose a novel photo-realistic text-to-image generation model that implicitly disentangles semantics to both fulfill the high-level semantic consistency and low-level semantic diversity. To be specific, we design (1) a Siamese mechanism in the discriminator to learn consistent high-level semantics, and (2) a visual-semantic embedding strategy by semantic-conditioned batch normalization to find diverse low-level semantics. Extensive experiments and ablation studies on CUB and MS-COCO datasets demonstrate the superiority of the proposed method in comparison to state-of-the-art methods.