# Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing

arXiv: https://arxiv.org/abs/2104.07021 · HTML: https://mlsanity.com/paper/2104.07021/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 47 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 30 of 32)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [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)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis](https://mlsanity.com/paper/1701.02096/) (2017)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [Self-Correction for Human Parsing](https://mlsanity.com/paper/1910.09777/) (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)
- [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)
- [A Variational U-Net for Conditional Appearance and Shape Generation](https://mlsanity.com/paper/1804.04694/) (2018)
- [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)
- [Towards Multi-pose Guided Virtual Try-on Network](https://mlsanity.com/paper/1902.11026/) (2019)
- [Dense Pose Transfer](https://mlsanity.com/paper/1809.01995/) (2018)
- [XingGAN for Person Image Generation](https://mlsanity.com/paper/2007.09278/) (2020)
- [Neural Re-Rendering of Humans from a Single Image](https://mlsanity.com/paper/2101.04104/) (2021)
- [Style and Pose Control for Image Synthesis of Humans from a Single Monocular View](https://mlsanity.com/paper/2102.11263/) (2021)
- [Deep Human Parsing with Active Template Regression](https://mlsanity.com/paper/1503.02391/) (2015)
- [The Conditional Analogy GAN: Swapping Fashion Articles on People Images](https://mlsanity.com/paper/1709.04695/) (2017)

## Built on by (most cited first, 30 of 54)

- [DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion](https://mlsanity.com/paper/2304.06025/) (2023)
- [Dress Code: High-Resolution Multi-Category Virtual Try-On](https://mlsanity.com/paper/2204.08532/) (2022)
- [Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks](https://mlsanity.com/paper/2501.15891/) (2025)
- [Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation](https://mlsanity.com/paper/2206.07771/) (2022)
- [Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN](https://mlsanity.com/paper/2111.10544/) (2021)
- [Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images](https://mlsanity.com/paper/2311.16094/) (2023)
- [Text2Human: Text-Driven Controllable Human Image Generation](https://mlsanity.com/paper/2205.15996/) (2022)
- [MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation](https://mlsanity.com/paper/2405.00448/) (2024)
- [AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario](https://mlsanity.com/paper/2405.18172/) (2024)
- [Dressing in the Wild by Watching Dance Videos](https://mlsanity.com/paper/2203.15320/) (2022)
- [SGDiff: A Style Guided Diffusion Model for Fashion Synthesis](https://mlsanity.com/paper/2308.07605/) (2023)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models](https://mlsanity.com/paper/2411.18350/) (2024)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on](https://mlsanity.com/paper/2207.13475/) (2022)
- [On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models](https://mlsanity.com/paper/2205.03859/) (2022)
- [Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems](https://mlsanity.com/paper/2111.15140/) (2021)
- [Training-free Geometric Image Editing on Diffusion Models](https://mlsanity.com/paper/2507.23300/) (2025)
- [Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning](https://mlsanity.com/paper/2211.14052/) (2022)
- [UniHuman: A Unified Model for Editing Human Images in the Wild](https://mlsanity.com/paper/2312.14985/) (2023)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [A Two-stage Personalized Virtual Try-on Framework with Shape Control and Texture Guidance](https://mlsanity.com/paper/2312.15480/) (2023)
- [Conditional Temporal Variational AutoEncoder for Action Video Prediction](https://mlsanity.com/paper/2108.05658/) (2021)
- [FashionTex: Controllable Virtual Try-on with Text and Texture](https://mlsanity.com/paper/2305.04451/) (2023)
- [PG-VTON: A Novel Image-Based Virtual Try-On Method via Progressive Inference Paradigm](https://mlsanity.com/paper/2304.08956/) (2023)
- [Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures](https://mlsanity.com/paper/2210.01887/) (2022)
- [VGFlow: Visibility guided Flow Network for Human Reposing](https://mlsanity.com/paper/2211.08540/) (2022)
- [Learning Garment DensePose for Robust Warping in Virtual Try-On](https://mlsanity.com/paper/2303.17688/) (2023)
- [Total Selfie: Generating Full-Body Selfies](https://mlsanity.com/paper/2308.14740/) (2023)
- [Design2Cloth: 3D Cloth Generation from 2D Masks](https://mlsanity.com/paper/2404.02686/) (2024)

## Abstract (arXiv)

We propose a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks. The key to DiOr is a novel recurrent generation pipeline to sequentially put garments on a person, so that trying on the same garments in different orders will result in different looks. Our system can produce dressing effects not achievable by existing work, including different interactions of garments (e.g., wearing a top tucked into the bottom or over it), as well as layering of multiple garments of the same type (e.g., jacket over shirt over t-shirt). DiOr explicitly encodes the shape and texture of each garment, enabling these elements to be edited separately. Joint training on pose transfer and inpainting helps with detail preservation and coherence of generated garments. Extensive evaluations show that DiOr outperforms other recent methods like ADGAN in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision.