# GLocal: Global Graph Reasoning and Local Structure Transfer for Person Image Generation

arXiv: https://arxiv.org/abs/2112.00263 · HTML: https://mlsanity.com/paper/2112.00263/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 29 references that are in the map.
- First posted on arXiv 1 Dec 2021.

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

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [SEAN: Image Synthesis with Semantic Region-Adaptive Normalization](https://mlsanity.com/paper/1911.12861/) (2019)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Deformable GANs for Pose-based Human Image Generation](https://mlsanity.com/paper/1801.00055/) (2017)
- [Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis](https://mlsanity.com/paper/1909.12224/) (2019)
- [Controllable Person Image Synthesis with Attribute-Decomposed GAN](https://mlsanity.com/paper/2003.12267/) (2020)
- [Progressive Pose Attention Transfer for Person Image Generation](https://mlsanity.com/paper/1904.03349/) (2019)
- [Dense Intrinsic Appearance Flow for Human Pose Transfer](https://mlsanity.com/paper/1903.11326/) (2019)
- [XingGAN for Person Image Generation](https://mlsanity.com/paper/2007.09278/) (2020)
- [PISE: Person Image Synthesis and Editing with Decoupled GAN](https://mlsanity.com/paper/2103.04023/) (2021)
- [Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis](https://mlsanity.com/paper/1810.11610/) (2018)
- [Efficient Semantic Image Synthesis via Class-Adaptive Normalization](https://mlsanity.com/paper/2012.04644/) (2020)
- [Unsupervised Person Image Synthesis in Arbitrary Poses](https://mlsanity.com/paper/1809.10280/) (2018)
- [Diverse Semantic Image Synthesis via Probability Distribution Modeling](https://mlsanity.com/paper/2103.06878/) (2021)
- [Learning Semantic Person Image Generation by Region-Adaptive Normalization](https://mlsanity.com/paper/2104.06650/) (2021)
- [Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation](https://mlsanity.com/paper/2008.12606/) (2020)
- [Unbalanced Feature Transport for Exemplar-based Image Translation](https://mlsanity.com/paper/2106.10482/) (2021)
- [Bipartite Graph Reasoning GANs for Person Image Generation](https://mlsanity.com/paper/2008.04381/) (2020)
- [PoNA: Pose-guided Non-local Attention for Human Pose Transfer](https://mlsanity.com/paper/2012.07049/) (2020)
- [GAC-GAN: A General Method for Appearance-Controllable Human Video Motion Transfer](https://mlsanity.com/paper/1911.10672/) (2019)
- [C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer](https://mlsanity.com/paper/2012.08976/) (2020)
- [Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation](https://mlsanity.com/paper/2102.02972/) (2021)
- [Pose-Guided High-Resolution Appearance Transfer via Progressive Training](https://mlsanity.com/paper/2008.11898/) (2020)

## Abstract (arXiv)

In this paper, we focus on person image generation, namely, generating person image under various conditions, e.g., corrupted texture or different pose. To address texture occlusion and large pose misalignment in this task, previous works just use the corresponding region's style to infer the occluded area and rely on point-wise alignment to reorganize the context texture information, lacking the ability to globally correlate the region-wise style codes and preserve the local structure of the source. To tackle these problems, we present a GLocal framework to improve the occlusion-aware texture estimation by globally reasoning the style inter-correlations among different semantic regions, which can also be employed to recover the corrupted images in texture inpainting. For local structural information preservation, we further extract the local structure of the source image and regain it in the generated image via local structure transfer. We benchmark our method to fully characterize its performance on DeepFashion dataset and present extensive ablation studies that highlight the novelty of our method.