Enhanced Realism in Virtual Try-On Tasks Using Diffusion Methods
Virtual try-on technology is revolutionizing online retail by enabling customers to visualize garments on their bodies before purchasing. Traditional methods, often based on Generative Adversarial Networks (GANs), face challenges such as misalignment and visual artifacts, especially in complex poses...
Gespeichert in:
| Veröffentlicht in: | 2025 11th International Conference on Computing and Artificial Intelligence (ICCAI) S. 128 - 133 |
|---|---|
| Hauptverfasser: | , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
28.03.2025
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Virtual try-on technology is revolutionizing online retail by enabling customers to visualize garments on their bodies before purchasing. Traditional methods, often based on Generative Adversarial Networks (GANs), face challenges such as misalignment and visual artifacts, especially in complex poses. We present a virtual try-on framework leveraging diffusion models to enhance realism, accuracy, and garment detail preservation. Our approach integrates Vector Quantized Variational Autoencoders (VQ-VAEs) for precise feature matching within a diffusion U-Net architecture. By adopting image-based conditioning with the CLIP image encoder, our system utilizes visual features directly from clothing images for more faithful garment representations. Additionally, an Additional Feature Preserving Block (ControlNet) maintains intricate details like textures and logos, addressing fine-grained garment fidelity challenges. Quantitative evaluation demonstrates our system's superior performance, achieving the best LPIPS of 0.082. We also achieve a Fréchet Inception Distance (FID) of 7.782 and Kernel Inception Distance (KID) of 1.53, indicating enhanced image quality and feature alignment. Although the Structural Similarity Index Measure (SSIM) of \mathbf{0. 8 2 5} is slightly lower, it underscores the trade-off for improved realism and garment detail preservation. Our contributions set a new benchmark for accurate and realistic clothing visualization in virtual try-on systems. |
|---|---|
| AbstractList | Virtual try-on technology is revolutionizing online retail by enabling customers to visualize garments on their bodies before purchasing. Traditional methods, often based on Generative Adversarial Networks (GANs), face challenges such as misalignment and visual artifacts, especially in complex poses. We present a virtual try-on framework leveraging diffusion models to enhance realism, accuracy, and garment detail preservation. Our approach integrates Vector Quantized Variational Autoencoders (VQ-VAEs) for precise feature matching within a diffusion U-Net architecture. By adopting image-based conditioning with the CLIP image encoder, our system utilizes visual features directly from clothing images for more faithful garment representations. Additionally, an Additional Feature Preserving Block (ControlNet) maintains intricate details like textures and logos, addressing fine-grained garment fidelity challenges. Quantitative evaluation demonstrates our system's superior performance, achieving the best LPIPS of 0.082. We also achieve a Fréchet Inception Distance (FID) of 7.782 and Kernel Inception Distance (KID) of 1.53, indicating enhanced image quality and feature alignment. Although the Structural Similarity Index Measure (SSIM) of \mathbf{0. 8 2 5} is slightly lower, it underscores the trade-off for improved realism and garment detail preservation. Our contributions set a new benchmark for accurate and realistic clothing visualization in virtual try-on systems. |
| Author | Phoomvuthisarn, Suronapee Kiattithapanayong, Saris |
| Author_xml | – sequence: 1 givenname: Saris surname: Kiattithapanayong fullname: Kiattithapanayong, Saris email: saris.kiat@gmail.com organization: Chulalongkorn University,Faculty of Commerce and Accountancy,Department of Statistics,Bangkok,Thailand – sequence: 2 givenname: Suronapee surname: Phoomvuthisarn fullname: Phoomvuthisarn, Suronapee email: suronapee@cbs.chula.ac.th organization: Chulalongkorn University,Faculty of Commerce and Accountancy,Department of Statistics,Bangkok,Thailand |
| BookMark | eNotjctOwzAUBY0ECyj9A5D8Awm-ftXeUYVSIhVVagPbyvGDWqQOitNF_55IcDazGc25Q9epTx6hRyAlANFPdVUtaykFgZISKkpCCFVXaK4XWjEGgnIN7BY9r9LRJOsd3nnTxXzCMeHPOIxn0-FmuBTbhBuTvzP-yDF94ZcYwjnHPuF3Px57l-_RTTBd9vN_ztD-ddVUb8Vmu66r5aaImo0F59pOz5RAa62kllhwLSzA0sCZY9JJJ5iDaSJQzYxWXFCvnW6d8kaxGXr4q0bv_eFniCczXA6TToTikv0C2jlGCw |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICCAI66501.2025.00028 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9798331524913 |
| EndPage | 133 |
| ExternalDocumentID | 11105846 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i93t-449c979201bcc62c0c1db171c2f43d36d6d53d11115f293a98452e9d9bd8ea83 |
| IEDL.DBID | RIE |
| IngestDate | Wed Aug 20 06:20:56 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-449c979201bcc62c0c1db171c2f43d36d6d53d11115f293a98452e9d9bd8ea83 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_11105846 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-March-28 |
| PublicationDateYYYYMMDD | 2025-03-28 |
| PublicationDate_xml | – month: 03 year: 2025 text: 2025-March-28 day: 28 |
| PublicationDecade | 2020 |
| PublicationTitle | 2025 11th International Conference on Computing and Artificial Intelligence (ICCAI) |
| PublicationTitleAbbrev | ICCAI |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.9034373 |
| Snippet | Virtual try-on technology is revolutionizing online retail by enabling customers to visualize garments on their bodies before purchasing. Traditional methods,... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 128 |
| SubjectTerms | Accuracy Autoencoders Benchmark testing Computer architecture ControlNet Diffusion models Diffusions Kernel Training Vectors Virtual Tryon Visualization |
| Title | Enhanced Realism in Virtual Try-On Tasks Using Diffusion Methods |
| URI | https://ieeexplore.ieee.org/document/11105846 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA62ePCkYsU3OXiN3Tw2j5tSW-zBWrRIb2U3yeIibstuK_jvzaT1cfHgLQSSkITMl0zmmw-hS8dEIY1yhGaaE6G8IFmSU2JTkbFC62A4RRSbUKORnk7NeENWj1wY730MPvNXUIx_-W5uV-Aq64ZzmQBgtlBLKbkma21YOTQx3WGvdzOU4coB7z4GvpIERNZ_qaZE0Bjs_nO4PdT5od_h8Tew7KMtXx2g6371En_r8aOHpIVvuKzwc1kD_wNP6g_yUOFJ1rw2OEYB4NuyKFbgCsP3USS66aCnQX_SuyMb-QNSGr4kQhhrlAkAnVsrmU0sdTlV1LJCcMelky7lDixeWgTMzowWKfPGmdxpHxb-ELWreeWPEA52BBizlMqYPU6bJPSa83B54rkKrY5RByY_W6zzW8y-5n3yR_0p2oH1hUgsps9Qe1mv_Dnatu_Lsqkv4q58AtG7jEw |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFA86BT2pOPHbHLzGNR9tk5syNxxuc2iR3UabpFjETtpN8L83L9aPiwdvIZCEJOT9kpf3ez-Ezg0TeaRiQ2gqORGxFSQNMkp0KFKWS-kMp_BiE_F4LKdTNWnI6p4LY631wWf2Aor-L9_M9RJcZR13LgMAzFW0BtJZDV2r4eXQQHUG3e7VIHKXDnj5MfCWBCCz_ks3xcNGf-ufA26j9g8BD0--oWUHrdhyF132yif_X4_vLaQtfMFFiR-LChggOKneyV2Jk7R-rrGPA8DXRZ4vwRmGR14mum6jh34v6d6QRgCBFIoviBBKq1g5iM60jpgONDUZjalmueCGRyYyITdg88LcoXaqpAiZVUZlRlq39HuoVc5Lu4-wsyTAmaU08vnjpApcrxl31yeexa7VAWrD5GevnxkuZl_zPvyj_gxt3CSj4Ww4GN8eoU1Ya4jLYvIYtRbV0p6gdf22KOrq1O_QB1X6j5U |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2025+11th+International+Conference+on+Computing+and+Artificial+Intelligence+%28ICCAI%29&rft.atitle=Enhanced+Realism+in+Virtual+Try-On+Tasks+Using+Diffusion+Methods&rft.au=Kiattithapanayong%2C+Saris&rft.au=Phoomvuthisarn%2C+Suronapee&rft.date=2025-03-28&rft.pub=IEEE&rft.spage=128&rft.epage=133&rft_id=info:doi/10.1109%2FICCAI66501.2025.00028&rft.externalDocID=11105846 |