# Learning Semantic Person Image Generation by Region-Adaptive Normalization

arXiv: https://arxiv.org/abs/2104.06650 · HTML: https://mlsanity.com/paper/2104.06650/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 47 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [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)
- [Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Content](https://mlsanity.com/paper/2003.05863/) (2020)
- [Progressive Pose Attention Transfer for Person Image Generation](https://mlsanity.com/paper/1904.03349/) (2019)
- [Instance-level Human Parsing via Part Grouping Network](https://mlsanity.com/paper/1808.00157/) (2018)
- [A Variational U-Net for Conditional Appearance and Shape Generation](https://mlsanity.com/paper/1804.04694/) (2018)
- [Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis](https://mlsanity.com/paper/1910.06809/) (2019)
- [Dense Intrinsic Appearance Flow for Human Pose Transfer](https://mlsanity.com/paper/1903.11326/) (2019)
- [Deep Image Spatial Transformation for Person Image Generation](https://mlsanity.com/paper/2003.00696/) (2020)
- [Synthesizing Images of Humans in Unseen Poses](https://mlsanity.com/paper/1804.07739/) (2018)
- [Dense Pose Transfer](https://mlsanity.com/paper/1809.01995/) (2018)
- [XingGAN for Person Image Generation](https://mlsanity.com/paper/2007.09278/) (2020)
- [Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation](https://mlsanity.com/paper/1912.12215/) (2019)
- [Pose-Normalized Image Generation for Person Re-identification](https://mlsanity.com/paper/1712.02225/) (2017)
- [Pose Guided Human Video Generation](https://mlsanity.com/paper/1807.11152/) (2018)

## Built on by (most cited first, 30 of 44)

- [HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation](https://mlsanity.com/paper/2304.04269/) (2023)
- [Person Image Synthesis via Denoising Diffusion Model](https://mlsanity.com/paper/2211.12500/) (2022)
- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
- [Exploring Dual-task Correlation for Pose Guided Person Image Generation](https://mlsanity.com/paper/2203.02910/) (2022)
- [Freestyle Layout-to-Image Synthesis](https://mlsanity.com/paper/2303.14412/) (2023)
- [Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis](https://mlsanity.com/paper/2402.18078/) (2024)
- [Cross Attention Based Style Distribution for Controllable Person Image Synthesis](https://mlsanity.com/paper/2208.00712/) (2022)
- [3D-Aware Semantic-Guided Generative Model for Human Synthesis](https://mlsanity.com/paper/2112.01422/) (2021)
- [Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfer](https://mlsanity.com/paper/2307.07754/) (2023)
- [MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware Diffusion and Iterative Refinement](https://mlsanity.com/paper/2408.14211/) (2024)
- [GRPose: Learning Graph Relations for Human Image Generation with Pose Priors](https://mlsanity.com/paper/2408.16540/) (2024)
- [UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer](https://mlsanity.com/paper/2304.08870/) (2023)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [UnitedHuman: Harnessing Multi-Source Data for High-Resolution Human Generation](https://mlsanity.com/paper/2309.14335/) (2023)
- [BeyondScene: Higher-Resolution Human-Centric Scene Generation With Pretrained Diffusion](https://mlsanity.com/paper/2404.04544/) (2024)
- [Location-aware Adaptive Normalization: A Deep Learning Approach For Wildfire Danger Forecasting](https://mlsanity.com/paper/2212.08208/) (2022)
- [Enhancing Object Coherence in Layout-to-Image Synthesis](https://mlsanity.com/paper/2311.10522/) (2023)
- [DRAN: Detailed Region-Adaptive Normalization for Conditional Image Synthesis](https://mlsanity.com/paper/2109.14525/) (2021)
- [Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures](https://mlsanity.com/paper/2210.01887/) (2022)
- [DiffBody: Diffusion-based Pose and Shape Editing of Human Images](https://mlsanity.com/paper/2401.02804/) (2024)
- [Multi-focal Conditioned Latent Diffusion for Person Image Synthesis](https://mlsanity.com/paper/2503.15686/) (2025)
- [VGFlow: Visibility guided Flow Network for Human Reposing](https://mlsanity.com/paper/2211.08540/) (2022)
- [Total Selfie: Generating Full-Body Selfies](https://mlsanity.com/paper/2308.14740/) (2023)
- [Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization](https://mlsanity.com/paper/2105.14739/) (2021)
- [Open-World Pose Transfer via Sequential Test-Time Adaption](https://mlsanity.com/paper/2303.10945/) (2023)
- [Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation](https://mlsanity.com/paper/2309.00216/) (2023)
- [Disentangled Representation Learning for Controllable Person Image Generation](https://mlsanity.com/paper/2312.05798/) (2023)
- [Pose Guided Human Image Synthesis with Partially Decoupled GAN](https://mlsanity.com/paper/2210.03627/) (2022)
- [Enhanced Multi-Scale Cross-Attention for Person Image Generation](https://mlsanity.com/paper/2501.08900/) (2025)
- [Contrastive Monotonic Pixel-Level Modulation](https://mlsanity.com/paper/2207.11517/) (2022)

## Abstract (arXiv)

Human pose transfer has received great attention due to its wide applications, yet is still a challenging task that is not well solved. Recent works have achieved great success to transfer the person image from the source to the target pose. However, most of them cannot well capture the semantic appearance, resulting in inconsistent and less realistic textures on the reconstructed results. To address this issue, we propose a new two-stage framework to handle the pose and appearance translation. In the first stage, we predict the target semantic parsing maps to eliminate the difficulties of pose transfer and further benefit the latter translation of per-region appearance style. In the second one, with the predicted target semantic maps, we suggest a new person image generation method by incorporating the region-adaptive normalization, in which it takes the per-region styles to guide the target appearance generation. Extensive experiments show that our proposed SPGNet can generate more semantic, consistent, and photo-realistic results and perform favorably against the state of the art methods in terms of quantitative and qualitative evaluation. The source code and model are available at https://github.com/cszy98/SPGNet.git.