# FDA-GAN: Flow-based Dual Attention GAN for Human Pose Transfer

arXiv: https://arxiv.org/abs/2112.00281 · HTML: https://mlsanity.com/paper/2112.00281/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 57 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 30 of 43)

- [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)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (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)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [MoCoGAN: Decomposing Motion and Content for Video Generation](https://mlsanity.com/paper/1707.04993/) (2017)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Few-shot Video-to-Video Synthesis](https://mlsanity.com/paper/1910.12713/) (2019)
- [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)
- [Instance-level Human Parsing via Part Grouping Network](https://mlsanity.com/paper/1808.00157/) (2018)
- [Dense Intrinsic Appearance Flow for Human Pose Transfer](https://mlsanity.com/paper/1903.11326/) (2019)
- [Disentangled Person Image Generation](https://mlsanity.com/paper/1712.02621/) (2017)
- [Synthesizing Images of Humans in Unseen Poses](https://mlsanity.com/paper/1804.07739/) (2018)
- [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)
- [Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction](https://mlsanity.com/paper/1909.13245/) (2019)
- [Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation](https://mlsanity.com/paper/2008.12606/) (2020)

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

- [Pose-Guided Fine-Grained Sign Language Video Generation](https://mlsanity.com/paper/2409.16709/) (2024)
- [PG-VTON: A Novel Image-Based Virtual Try-On Method via Progressive Inference Paradigm](https://mlsanity.com/paper/2304.08956/) (2023)
- [DiffBody: Diffusion-based Pose and Shape Editing of Human Images](https://mlsanity.com/paper/2401.02804/) (2024)
- [VGFlow: Visibility guided Flow Network for Human Reposing](https://mlsanity.com/paper/2211.08540/) (2022)
- [MD-Face: MoE-Enhanced Label-Free Disentangled Representation for Interactive Facial Attribute Editing](https://mlsanity.com/paper/2604.20317/) (2026)

## Abstract (arXiv)

Human pose transfer aims at transferring the appearance of the source person to the target pose. Existing methods utilizing flow-based warping for non-rigid human image generation have achieved great success. However, they fail to preserve the appearance details in synthesized images since the spatial correlation between the source and target is not fully exploited. To this end, we propose the Flow-based Dual Attention GAN (FDA-GAN) to apply occlusion- and deformation-aware feature fusion for higher generation quality. Specifically, deformable local attention and flow similarity attention, constituting the dual attention mechanism, can derive the output features responsible for deformable- and occlusion-aware fusion, respectively. Besides, to maintain the pose and global position consistency in transferring, we design a pose normalization network for learning adaptive normalization from the target pose to the source person. Both qualitative and quantitative results show that our method outperforms state-of-the-art models in public iPER and DeepFashion datasets.