# SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On

arXiv: https://arxiv.org/abs/2001.06265 · HTML: https://mlsanity.com/paper/2001.06265/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

## Built on by (most cited first, 23 of 23)

- [Dress Code: High-Resolution Multi-Category Virtual Try-On](https://mlsanity.com/paper/2204.08532/) (2022)
- [High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions](https://mlsanity.com/paper/2206.14180/) (2022)
- [ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors](https://mlsanity.com/paper/2109.07001/) (2021)
- [Toward Accurate and Realistic Outfits Visualization with Attention to Details](https://mlsanity.com/paper/2106.06593/) (2021)
- [TryOnGAN: Body-Aware Try-On via Layered Interpolation](https://mlsanity.com/paper/2101.02285/) (2021)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [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)
- [Shape Controllable Virtual Try-on for Underwear Models](https://mlsanity.com/paper/2107.13156/) (2021)
- [Limb-Aware Virtual Try-On Network with Progressive Clothing Warping](https://mlsanity.com/paper/2503.14074/) (2025)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Progressive Limb-Aware Virtual Try-On](https://mlsanity.com/paper/2503.12588/) (2025)
- [An Efficient Style Virtual Try on Network for Clothing Business Industry](https://mlsanity.com/paper/2105.13183/) (2021)
- [PT-VTON: an Image-Based Virtual Try-On Network with Progressive Pose Attention Transfer](https://mlsanity.com/paper/2111.12167/) (2021)
- [WG-VITON: Wearing-Guide Virtual Try-On for Top and Bottom Clothes](https://mlsanity.com/paper/2205.04759/) (2022)
- [Significance of Skeleton-based Features in Virtual Try-On](https://mlsanity.com/paper/2208.08076/) (2022)
- [Fill in Fabrics: Body-Aware Self-Supervised Inpainting for Image-Based Virtual Try-On](https://mlsanity.com/paper/2210.00918/) (2022)
- [ClothFit: Cloth-Human-Attribute Guided Virtual Try-On Network Using 3D Simulated Dataset](https://mlsanity.com/paper/2306.13908/) (2023)
- [Realistic, Animatable Human Reconstructions for Virtual Fit-On](https://mlsanity.com/paper/2210.08535/) (2022)
- [Significance of Anatomical Constraints in Virtual Try-On](https://mlsanity.com/paper/2401.02110/) (2024)
- [High-Fidelity Virtual Try-on with Large-Scale Unpaired Learning](https://mlsanity.com/paper/2411.01593/) (2024)
- [EOPose : Exemplar-based object reposing using Generalized Pose Correspondences](https://mlsanity.com/paper/2505.03394/) (2025)

## Abstract (arXiv)

Image-based virtual try-on for fashion has gained considerable attention recently. The task requires trying on a clothing item on a target model image. An efficient framework for this is composed of two stages: (1) warping (transforming) the try-on cloth to align with the pose and shape of the target model, and (2) a texture transfer module to seamlessly integrate the warped try-on cloth onto the target model image. Existing methods suffer from artifacts and distortions in their try-on output. In this work, we present SieveNet, a framework for robust image-based virtual try-on. Firstly, we introduce a multi-stage coarse-to-fine warping network to better model fine-grained intricacies (while transforming the try-on cloth) and train it with a novel perceptual geometric matching loss. Next, we introduce a try-on cloth conditioned segmentation mask prior to improve the texture transfer network. Finally, we also introduce a dueling triplet loss strategy for training the texture translation network which further improves the quality of the generated try-on results. We present extensive qualitative and quantitative evaluations of each component of the proposed pipeline and show significant performance improvements against the current state-of-the-art method.