Papers › CP-VTON+: Clothing Shape and Texture Preserving Image-Based Virtual Try-On
CP-VTON+: Clothing Shape and Texture Preserving Image-Based Virtual Try-On
Matiur Rahman Minar, Thai Thanh Tuan, Heejune Ahn, Paul Rosin, Yu-Kun Lai
Recently proposed Image-based virtual try-on (VTON) approaches have several challenges regarding diverse human poses and cloth styles. First, clothing warping networks often generate highly distorted and misaligned warped clothes, due to the erroneous clothing-agnostic human representations, mismatches in input images for clothing-human matching, and improper regularization transform parameters. Second, blending networks can fail to retain the remaining clothes due to the wrong human representation and improper training loss for composition mask generation. We propose CP-VTON+ (Clothing shape and texture Preserving VTON) to overcome these issues, which significantly outperforms the state-of-the-art methods, both quantitatively and qualitatively.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Virtual Try-on | VITON | CP-VTON+ | IS | 3.1048 | #10 of 10 | Archive leaderboard | report |
| Virtual Try-on | VITON | CP-VTON+ | LPIPS | 0.1144 | #10 of 10 | Archive leaderboard | report |
| Virtual Try-on | VITON | CP-VTON+ | SSIM | 0.8163 | #10 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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