Towards Squeezing-Averse Virtual Try-On via Sequential Deformation
December 26, 2023 ยท Entered Twilight ยท ๐ AAAI Conference on Artificial Intelligence
Repo contents: README.md, assets, cp_dataset.py, cp_dataset_test.py, eval_models, evaluate.py, network_generator.py, networks.py, pg_modules, sync_batchnorm, test_generator.py, train_condition.py, train_generator.py, utils.py
Authors
Sang-Heon Shim, Jiwoo Chung, Jae-Pil Heo
arXiv ID
2312.15861
Category
cs.CV: Computer Vision
Citations
20
Venue
AAAI Conference on Artificial Intelligence
Repository
https://github.com/SHShim0513/SD-VITON
โญ 138
Last Checked
1 month ago
Abstract
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.
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