# Adversarial Synthesis of Human Pose from Text

arXiv: https://arxiv.org/abs/2005.00340 · HTML: https://mlsanity.com/paper/2005.00340/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

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

- [Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation](https://mlsanity.com/paper/2305.09662/) (2023)
- [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)
- [Towards Better Adversarial Synthesis of Human Images from Text](https://mlsanity.com/paper/2107.01869/) (2021)
- [Text-Conditional Contextualized Avatars For Zero-Shot Personalization](https://mlsanity.com/paper/2304.07410/) (2023)
- [GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation](https://mlsanity.com/paper/2409.11689/) (2024)
- [FashionPose: Unified Text-Driven Fashion Synthesis with Joint Geometric and Photometric Control](https://mlsanity.com/paper/2507.13311/) (2025)

## Abstract (arXiv)

This work focuses on synthesizing human poses from human-level text descriptions. We propose a model that is based on a conditional generative adversarial network. It is designed to generate 2D human poses conditioned on human-written text descriptions. The model is trained and evaluated using the COCO dataset, which consists of images capturing complex everyday scenes with various human poses. We show through qualitative and quantitative results that the model is capable of synthesizing plausible poses matching the given text, indicating that it is possible to generate poses that are consistent with the given semantic features, especially for actions with distinctive poses.