mlsanityData to 14 Oct 2026

Towards Squeezing-Averse Virtual Try-On via Sequential Deformation

arXiv 2312.15861 ↗First posted 26 Dec 2023cs.CVcited by 18

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Abstract (arXiv)

In this paper, we first investigate a visual quality degradation problem observed in recent high-resolution virtual try-on approach. The tendency is empirically found that the textures of clothes are squeezed at the sleeve, as visualized in the upper row of Fig.1(a). A main reason for the issue arises from a gradient conflict between two popular losses, the Total Variation (TV) and adversarial losses. Specifically, the TV loss aims to disconnect boundaries between the sleeve and torso in a warped clothing mask, whereas the adversarial loss aims to combine between them. Such contrary objectives feedback the misaligned gradients to a cascaded appearance flow estimation, resulting in undesirable squeezing artifacts. To reduce this, we propose a Sequential Deformation (SD-VITON) that disentangles the appearance flow prediction layers into TV objective-dominant (TVOB) layers and a task-coexistence (TACO) layer. Specifically, we coarsely fit the clothes onto a human body via the TVOB layers, and then keep on refining via the TACO layer. In addition, the bottom row of Fig.1(a) shows a different type of squeezing artifacts around the waist. To address it, we further propose that we first warp the clothes into a tucked-out shirts style, and then partially erase the texture from the warped clothes without hurting the smoothness of the appearance flows. Experimental results show that our SD-VITON successfully resolves both types of artifacts and outperforms the baseline methods. Source code will be available at https://github.com/SHShim0513/SD-VITON.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 26 Dec 2023.

Builds on

10 of 15 references.

References · 10

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  2. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  3. 2018DensePose: Dense Human Pose Estimation In The Wildcited by 575
  4. 2021VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalizationcited by 287
  5. 2017VITON: An Image-based Virtual Try-on Networkcited by 286
  6. 2018Toward Characteristic-Preserving Image-based Virtual Try-On Networkcited by 192
  7. 2016View Synthesis by Appearance Flowcited by 172
  8. 2020Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Contentcited by 149
  9. 2018Instance-level Human Parsing via Part Grouping Networkcited by 134
  10. 2022High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditionscited by 130

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 5; 2026: 7.

18 papers.

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  1. 2024Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videoscited by 32
  2. 2024Learning Flow Fields in Attention for Controllable Person Image Generationcited by 27
  3. 2024GaussianVTON: 3D Human Virtual Try-ON via Multi-Stage Gaussian Splatting Editing with Image Promptingcited by 22
  4. 2024D4-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-Oncited by 14
  5. 2024TED-VITON: Transformer-Empowered Diffusion Models for Virtual Try-Oncited by 8
  6. 2025MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Inputcited by 6
  7. 2025VTBench: Comprehensive Benchmark Suite Towards Real-World Virtual Try-on Modelscited by 6
  8. 2024Diversity-aware Channel Pruning for StyleGAN Compressioncited by 5
  9. 2025Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Schedulingcited by 3
  10. 2025DualFit: A Two-Stage Virtual Try-On via Warping and Synthesiscited by 2
  11. 2026OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluationcited by 2
  12. 2026Mobile-VTON: High-Fidelity On-Device Virtual Try-Oncited by 2
6 more
  1. 2025HYB-VITON: A Hybrid Approach to Virtual Try-On Combining Explicit and Implicit Warpingcited by 1
  2. 2026VTEdit-Bench: A Comprehensive Benchmark for Multi-Reference Image Editing Models in Virtual Try-Oncited by 1
  3. 2026SIFT-VTON: Geometric Correspondence Supervision on Cross-Attention for Virtual Try-Oncited by 1
  4. 2026Reference-Free Image Quality Assessment for Virtual Try-On via Human Feedbackcited by 0
  5. 2026MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-Oncited by 0
  6. 2026TexTailor: Texture-Preserving Video Virtual Try-On via Adaptive Garment Conditioningcited by 0