# Compatible and Diverse Fashion Image Inpainting

arXiv: https://arxiv.org/abs/1902.01096 · HTML: https://mlsanity.com/paper/1902.01096/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2019.

## Built on by (most cited first, 26 of 26)

- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [PD-GAN: Probabilistic Diverse GAN for Image Inpainting](https://mlsanity.com/paper/2105.02201/) (2021)
- [Disentangled Cycle Consistency for Highly-realistic Virtual Try-On](https://mlsanity.com/paper/2103.09479/) (2021)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation](https://mlsanity.com/paper/2109.10595/) (2021)
- [Picture that Sketch: Photorealistic Image Generation from Abstract Sketches](https://mlsanity.com/paper/2303.11162/) (2023)
- [Toward Accurate and Realistic Outfits Visualization with Attention to Details](https://mlsanity.com/paper/2106.06593/) (2021)
- [CT-Net: Complementary Transfering Network for Garment Transfer with Arbitrary Geometric Changes](https://mlsanity.com/paper/2105.05497/) (2021)
- [AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints](https://mlsanity.com/paper/2205.10636/) (2022)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [Texture Reformer: Towards Fast and Universal Interactive Texture Transfer](https://mlsanity.com/paper/2112.02788/) (2021)
- [View-consistent Object Removal in Radiance Fields](https://mlsanity.com/paper/2408.02100/) (2024)
- [ReGO: Reference-Guided Outpainting for Scenery Image](https://mlsanity.com/paper/2106.10601/) (2021)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing](https://mlsanity.com/paper/2111.12346/) (2021)
- [FashionSearchNet-v2: Learning Attribute Representations with Localization for Image Retrieval with Attribute Manipulation](https://mlsanity.com/paper/2111.14145/) (2021)
- [Deep Compatible Learning for Partially-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2206.09148/) (2022)
- [IS-Diff: Improving Diffusion-Based Inpainting with Better Initial Seed](https://mlsanity.com/paper/2509.11638/) (2025)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [Region-aware Attention for Image Inpainting](https://mlsanity.com/paper/2204.01004/) (2022)
- [Contextual Attention Mechanism, SRGAN Based Inpainting System for Eliminating Interruptions from Images](https://mlsanity.com/paper/2204.02591/) (2022)
- [An Analysis of Generative Methods for Multiple Image Inpainting](https://mlsanity.com/paper/2205.02146/) (2022)
- [Pluralistic Image Completion with Probabilistic Mixture-of-Experts](https://mlsanity.com/paper/2205.09086/) (2022)
- [DiCTI: Diffusion-based Clothing Designer via Text-guided Input](https://mlsanity.com/paper/2407.03901/) (2024)
- [Range-Edit: Semantic Mask Guided Outdoor LiDAR Scene Editing](https://mlsanity.com/paper/2511.17269/) (2025)
- [Wearing the Same Outfit in Different Ways -- A Controllable Virtual Try-on Method](https://mlsanity.com/paper/2211.16989/) (2022)

## Abstract (arXiv)

Visual compatibility is critical for fashion analysis, yet is missing in existing fashion image synthesis systems. In this paper, we propose to explicitly model visual compatibility through fashion image inpainting. To this end, we present Fashion Inpainting Networks (FiNet), a two-stage image-to-image generation framework that is able to perform compatible and diverse inpainting. Disentangling the generation of shape and appearance to ensure photorealistic results, our framework consists of a shape generation network and an appearance generation network. More importantly, for each generation network, we introduce two encoders interacting with one another to learn latent code in a shared compatibility space. The latent representations are jointly optimized with the corresponding generation network to condition the synthesis process, encouraging a diverse set of generated results that are visually compatible with existing fashion garments. In addition, our framework is readily extended to clothing reconstruction and fashion transfer, with impressive results. Extensive experiments with comparisons with state-of-the-art approaches on fashion synthesis task quantitatively and qualitatively demonstrate the effectiveness of our method.