# Towards Better Adversarial Synthesis of Human Images from Text

arXiv: https://arxiv.org/abs/2107.01869 · HTML: https://mlsanity.com/paper/2107.01869/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 27 of 27)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https://mlsanity.com/paper/1711.10485/) (2017)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [Neural 3D Mesh Renderer](https://mlsanity.com/paper/1711.07566/) (2017)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis](https://mlsanity.com/paper/1904.01310/) (2019)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [MirrorGAN: Learning Text-to-image Generation by Redescription](https://mlsanity.com/paper/1903.05854/) (2019)
- [StoryGAN: A Sequential Conditional GAN for Story Visualization](https://mlsanity.com/paper/1812.02784/) (2018)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [Generating 3D People in Scenes without People](https://mlsanity.com/paper/1912.02923/) (2019)
- [Object-driven Text-to-Image Synthesis via Adversarial Training](https://mlsanity.com/paper/1902.10740/) (2019)
- [Learning What and Where to Draw](https://mlsanity.com/paper/1610.02454/) (2016)
- [XingGAN for Person Image Generation](https://mlsanity.com/paper/2007.09278/) (2020)
- [Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments](https://mlsanity.com/paper/1903.05690/) (2019)
- [SMPLpix: Neural Avatars from 3D Human Models](https://mlsanity.com/paper/2008.06872/) (2020)
- [CycleGAN, a Master of Steganography](https://mlsanity.com/paper/1712.02950/) (2017)
- [Towards Diverse and Natural Image Descriptions via a Conditional GAN](https://mlsanity.com/paper/1703.06029/) (2017)
- [Be Your Own Prada: Fashion Synthesis with Structural Coherence](https://mlsanity.com/paper/1710.07346/) (2017)
- [Bipartite Graph Reasoning GANs for Person Image Generation](https://mlsanity.com/paper/2008.04381/) (2020)
- [Text Guided Person Image Synthesis](https://mlsanity.com/paper/1904.05118/) (2019)
- [Adversarial Synthesis of Human Pose from Text](https://mlsanity.com/paper/2005.00340/) (2020)

## Built on by (most cited first, 7 of 7)

- [ChatPose: Chatting about 3D Human Pose](https://mlsanity.com/paper/2311.18836/) (2023)
- [Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation](https://mlsanity.com/paper/2305.09662/) (2023)
- [UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing](https://mlsanity.com/paper/2411.16781/) (2024)
- [PoseScript: Linking 3D Human Poses and Natural Language](https://mlsanity.com/paper/2210.11795/) (2022)
- [TIPS: Text-Induced Pose Synthesis](https://mlsanity.com/paper/2207.11718/) (2022)
- [Text-Conditional Contextualized Avatars For Zero-Shot Personalization](https://mlsanity.com/paper/2304.07410/) (2023)
- [CoT-Pose: Chain-of-Thought Reasoning for 3D Pose Generation from Abstract Prompts](https://mlsanity.com/paper/2508.07540/) (2025)

## Abstract (arXiv)

This paper proposes an approach that generates multiple 3D human meshes from text. The human shapes are represented by 3D meshes based on the SMPL model. The model's performance is evaluated on the COCO dataset, which contains challenging human shapes and intricate interactions between individuals. The model is able to capture the dynamics of the scene and the interactions between individuals based on text. We further show how using such a shape as input to image synthesis frameworks helps to constrain the network to synthesize humans with realistic human shapes.