# Adversarial Synthesis of Human Pose from Text

Source: [Adversarial Synthesis of Human Pose from Text](<https://www.sota2.com/research/paper/68fa688c1e209131cea1ce19>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:44.961Z

Authors: Yifei Zhang, Rania Briq, Julian Tanke, Juergen Gall

Published: 2020-05-01T12:32:04.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2005.00340>)

[PDF](<https://arxiv.org/pdf/2005.00340.pdf>)