# Faces `a la Carte: Text-to-Face Generation via Attribute Disentanglement

arXiv: https://arxiv.org/abs/2006.07606 · HTML: https://mlsanity.com/paper/2006.07606/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2020.

## Built on by (most cited first, 16 of 16)

- [StyleGAN-Human: A Data-Centric Odyssey of Human Generation](https://mlsanity.com/paper/2204.11823/) (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)
- [StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis](https://mlsanity.com/paper/2203.15799/) (2022)
- [Audio-Visual Face Reenactment](https://mlsanity.com/paper/2210.02755/) (2022)
- [Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images](https://mlsanity.com/paper/2308.16758/) (2023)
- [Broadband Ground Motion Synthesis via Generative Adversarial Neural Operators: Development and Validation](https://mlsanity.com/paper/2309.03447/) (2023)
- [Vision + Language Applications: A Survey](https://mlsanity.com/paper/2305.14598/) (2023)
- [MM2Latent: Text-to-facial image generation and editing in GANs with multimodal assistance](https://mlsanity.com/paper/2409.11010/) (2024)
- [Improving Face Recognition from Caption Supervision with Multi-Granular Contextual Feature Aggregation](https://mlsanity.com/paper/2308.06866/) (2023)
- [GPTFace: Generative Pre-training of Facial-Linguistic Transformer by Span Masking and Weakly Correlated Text-image Data](https://mlsanity.com/paper/2510.18345/) (2025)
- [FaceCook: Face Generation Based on Linear Scaling Factors](https://mlsanity.com/paper/2109.03492/) (2021)
- [Face sketch to photo translation using generative adversarial networks](https://mlsanity.com/paper/2110.12290/) (2021)
- [Text-to-Face Generation with StyleGAN2](https://mlsanity.com/paper/2205.12512/) (2022)
- [Face Generation from Textual Features using Conditionally Trained Inputs to Generative Adversarial Networks](https://mlsanity.com/paper/2301.09123/) (2023)
- [Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation](https://mlsanity.com/paper/2506.14015/) (2025)

## Abstract (arXiv)

Text-to-Face (TTF) synthesis is a challenging task with great potential for diverse computer vision applications. Compared to Text-to-Image (TTI) synthesis tasks, the textual description of faces can be much more complicated and detailed due to the variety of facial attributes and the parsing of high dimensional abstract natural language. In this paper, we propose a Text-to-Face model that not only produces images in high resolution (1024x1024) with text-to-image consistency, but also outputs multiple diverse faces to cover a wide range of unspecified facial features in a natural way. By fine-tuning the multi-label classifier and image encoder, our model obtains the vectors and image embeddings which are used to transform the input noise vector sampled from the normal distribution. Afterwards, the transformed noise vector is fed into a pre-trained high-resolution image generator to produce a set of faces with the desired facial attributes. We refer to our model as TTF-HD. Experimental results show that TTF-HD generates high-quality faces with state-of-the-art performance.