# Attention-based Fusion for Multi-source Human Image Generation

arXiv: https://arxiv.org/abs/1905.02655 · HTML: https://mlsanity.com/paper/1905.02655/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [Graph2Pix: A Graph-Based Image to Image Translation Framework](https://mlsanity.com/paper/2108.09752/) (2021)
- [Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction](https://mlsanity.com/paper/2202.00843/) (2022)
- [Pose Guided Person Image Generation with Hidden p-Norm Regression](https://mlsanity.com/paper/2102.10033/) (2021)

## Abstract (arXiv)

We present a generalization of the person-image generation task, in which a human image is generated conditioned on a target pose and a set X of source appearance images. In this way, we can exploit multiple, possibly complementary images of the same person which are usually available at training and at testing time. The solution we propose is mainly based on a local attention mechanism which selects relevant information from different source image regions, avoiding the necessity to build specific generators for each specific cardinality of X. The empirical evaluation of our method shows the practical interest of addressing the person-image generation problem in a multi-source setting.