Towards Squeezing-Averse Virtual Try-On via Sequential Deformation
Sang-Heon Shim, Jiwoo Chung, Jae-Pil Heo
摘要
In this paper, we first investigate a visual quality degradation problem observed in recent high-resolution virtual tryon 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 tuckedout 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 .
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引用它的顶会 Paper7
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- FashionTailor: Controllable Clothing Editing for Human Images with Appearance PreservingJie Hou, Jianghong Ma, Xiangyu Mu, Haijun Zhang 等AAAI 2025 · 被引用 1 次
- MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-OnXiaoyu Han, Chenyang Wang, Jing Wang, Shunyuan Zheng 等CVPR 2026
- Learning Flow Fields in Attention for Controllable Person Image GenerationZijian Zhou, Shikun Liu, Xiao Han, Haozhe Liu 等CVPR 2025
它引用的顶会 Paper5
- ClothFlow: A Flow-Based Model for Clothed Person GenerationXintong Han, Weilin Huang, Xiaojun Hu, Matthew R. ScottICCV 2019 · 被引用 297 次
- Recon: Reducing Conflicting Gradients From the Root For Multi-Task LearningGuangyuan Shi, Qimai Li, Wenlong Zhang, Jiaxin Chen 等ICLR 2023 · 被引用 11 次
- Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image ContentHan Yang, Ruimao Zhang, Xiaobao Guo, Wei Liu 等CVPR 2020
- VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware NormalizationSeunghwan Choi, Sunghyun Park, Minsoo Lee, Jaegul ChooCVPR 2021
- Parser-Free Virtual Try-On via Distilling Appearance FlowsYuying Ge, Yibing Song, Ruimao Zhang, Chongjian Ge 等CVPR 2021
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