# Region-adaptive Texture Enhancement for Detailed Person Image Synthesis

arXiv: https://arxiv.org/abs/2005.12486 · HTML: https://mlsanity.com/paper/2005.12486/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 5 of 5)

- [Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfer](https://mlsanity.com/paper/2307.07754/) (2023)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [Spatial Content Alignment For Pose Transfer](https://mlsanity.com/paper/2103.16828/) (2021)
- [A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer](https://mlsanity.com/paper/2109.11719/) (2021)
- [Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail Generation](https://mlsanity.com/paper/2107.03742/) (2021)

## Abstract (arXiv)

The ability to produce convincing textural details is essential for the fidelity of synthesized person images. However, existing methods typically follow a ``warping-based'' strategy that propagates appearance features through the same pathway used for pose transfer. However, most fine-grained features would be lost due to down-sampling, leading to over-smoothed clothes and missing details in the output images. In this paper we presents RATE-Net, a novel framework for synthesizing person images with sharp texture details. The proposed framework leverages an additional texture enhancing module to extract appearance information from the source image and estimate a fine-grained residual texture map, which helps to refine the coarse estimation from the pose transfer module. In addition, we design an effective alternate updating strategy to promote mutual guidance between two modules for better shape and appearance consistency. Experiments conducted on DeepFashion benchmark dataset have demonstrated the superiority of our framework compared with existing networks.