Abstract
The fashion industry is undergoing a significant transformation, driven by advancements in digitalization and artificial intelligence (AI). This paper explores the integration of Stable Diffusion Models (SDMs) and AI to create high-quality images of synthetic models wearing a target cloth in multiple poses, enhancing the users’ experience on e-commerce websites and addressing the fast-paced demands of fashion trends. The proposed pipeline includes multiple steps: face generation, pose estimation, cloth warping, human synthesis, and refinement networks, each designed to enhance the realism and quality of the final images for e-commerce platforms. Experimental results on a small VITON-HD dataset demonstrate this approach’s overall success. BRISQUE, NIQE, and entropy were used for objective evaluation, scoring 14.1449, 4.1354, and 7.1238 respectively, indicating a high level of detail, naturalness, and complexity on the generated images. Future work should focus on enhancing the representation of clothing with complex patterns and lower and full garments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. 43(1), 172–186. https://doi.org/10.1109/TPAMI.2019.2929257, https://ieeexplore.ieee.org/document/8765346/
Chang, Z., Koulieris, G.A., Shum, H.P.H.: On the design fundamentals of diffusion models: a survey. https://doi.org/10.48550/ARXIV.2306.04542
Chen, C.Y., Lo, L., Huang, P.J., Shuai, H.H., Cheng, W.H.: FashionMirror: co-attention feature-remapping virtual try-on with sequential template poses. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 13789–13798. IEEE. https://doi.org/10.1109/ICCV48922.2021.01355, https://ieeexplore.ieee.org/document/9711025/
Chen, H.J., Shuai, H.H., Cheng, W.H.: A survey of artificial intelligence in fashion. 40(3), 64–73. https://doi.org/10.1109/MSP.2022.3233449, https://ieeexplore.ieee.org/document/10113373/
Choi, S., Park, S., Lee, M., Choo, J.: VITON-HD: high-resolution virtual try-on via misalignment-aware normalization. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14126–14135. IEEE. https://doi.org/10.1109/CVPR46437.2021.01391, https://ieeexplore.ieee.org/document/9578702/
Ge, Y., Song, Y., Zhang, R., Ge, C., Liu, W., Luo, P.: Parser-free virtual try-on via distilling appearance flows. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8481–8489. IEEE. https://doi.org/10.1109/CVPR46437.2021.00838, https://ieeexplore.ieee.org/document/9577989/
Gou, J., Sun, S., Zhang, J., Si, J., Qian, C., Zhang, L.: Taming the power of diffusion models for high-quality virtual try-on with appearance flow. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 7599–7607. ACM. https://doi.org/10.1145/3581783.3612255, https://dl.acm.org/doi/10.1145/3581783.3612255
Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: VITON: an image-based virtual try-on network . https://doi.org/10.48550/ARXIV.1711.08447, https://arxiv.org/abs/1711.08447
Hashimoto, N., Katsurai, M., Goto, R.: A visualization interface for exploring similar brands on a fashion E-commerce platform. In: 2021 IEEE International Conference on Web Services (ICWS), pp. 642–644. IEEE. https://doi.org/10.1109/ICWS53863.2021.00086, https://ieeexplore.ieee.org/document/9590368/
Kulkarni, A., Shivananda, A., Kulkarni, A., Gudivada, D.: Applied generative AI for beginners: practical knowledge on diffusion models, ChatGPT, and other LLMs. Apress. https://doi.org/10.1007/978-1-4842-9994-4, https://link.springer.com/10.1007/978-1-4842-9994-4
Liu, L., Zhang, H., Zhou, D., Shi, J.: Toward fashion intelligence in the big data era: state-of-the-art and future prospects, 1. https://doi.org/10.1109/TCE.2023.3285880, https://ieeexplore.ieee.org/document/10153335/
Lu, P., Li, Y., Jin, L., Han, S.: Blind image quality assessment based on wavelet power spectrum in perceptual domain. Trans. Tianjin Univ. 22(6), 596–602 (2016). https://doi.org/10.1007/s12209-016-2726-7
Mittal, A., Soundararajan, R., Bovik, A.C.: Making a completely blind image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013). https://doi.org/10.1109/LSP.2012.2227726
Morelli, D., Baldrati, A., Cartella, G., Cornia, M., Bertini, M., Cucchiara, R.: LaDI-VTON: latent diffusion textual-inversion enhanced virtual try-on. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 8580–8589. ACM. https://doi.org/10.1145/3581783.3612137
Narasimhaswamy, S., Bhattacharya, U., Chen, X., Dasgupta, I., Mitra, S., Hoai, M.: HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances (2024)
Qi, J., Deng, Y., Wang, Q., Yang, Z., Han, X., Li, Y.: Non-reference image quality assessment based on super-pixel segmentation and information entropy. In: 2021 IEEE 9th International Conference on Computer Science and Network Technology (ICCSNT), pp. 110–114 (2021). https://doi.org/10.1109/ICCSNT53786.2021.9615399
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation . https://doi.org/10.48550/ARXIV.1505.04597, https://arxiv.org/abs/1505.04597
Shen, H.W., Liu, T.J., Fan, C.M., Liu, K.H.: WBTP-VTON: whole body and texture preservation based virtual try-on network. In: 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 1–2. IEEE. https://doi.org/10.1109/ICCE-TW52618.2021.9603061, https://ieeexplore.ieee.org/document/9603061/
Tsai, D.Y., Lee, Y., Matsuyama, E.: Information entropy measure for evaluation of image quality. J. Digit. Imaging 21(3), 338–347 (2008). https://doi.org/10.1007/s10278-007-9044-5
Vazquez, E.E., Patel, C., Alvidrez, S., Siliceo, L.: Images, reviews, and purchase intention on social commerce: the role of mental imagery vividness, cognitive and affective social presence. J. Retail. Consum. Serv. 74, 103415 (2023). https://doi.org/10.1016/j.jretconser.2023.103415, https://www.sciencedirect.com/science/article/pii/S0969698923001625
Xu, J., Pu, Y., Nie, R., Xu, D., Zhao, Z., Qian, W.: Virtual try-on network with attribute transformation and local rendering. 23, 2222–2234. https://doi.org/10.1109/TMM.2021.3070972, https://ieeexplore.ieee.org/document/9397349/
Xu, M., Chen, Y., Liu, S., Li, T.H., Li, G.: Structure-transformed texture-enhanced network for person image synthesis. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 13839–13848. IEEE. https://doi.org/10.1109/ICCV48922.2021.01360, https://ieeexplore.ieee.org/document/9710223/
Yang, F., Lin, G.: CT-Net: Complementary transfering network for garment transfer with arbitrary geometric changes. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9894–9903. IEEE. https://doi.org/10.1109/CVPR46437.2021.00977, https://ieeexplore.ieee.org/document/9578127/
Ye, H., Zhang, J., Liu, S., Han, X., Yang, W.: IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models (2023)
Acknowledgement
This research work was funded by National Funds through the Portuguese FCT - Fundação para a Ciência e a Tecnologia under the R&D Units Project Scope UIDB/00760/2020 (https://doi.org/10.54499/UIDB/00760/2020) and UIDP/00760/2020 (https://doi.org/10.54499/UIPB/00760/2020).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Duarte, A.R., Conceição, L. (2025). Bridging Fashion and Technology: Synthetic Human Models for an Enhanced E-Commerce Experience. In: Santos, M.F., Machado, J., Novais, P., Cortez, P., Moreira, P.M. (eds) Progress in Artificial Intelligence. EPIA 2024. Lecture Notes in Computer Science(), vol 14967. Springer, Cham. https://doi.org/10.1007/978-3-031-73497-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-73497-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-73496-0
Online ISBN: 978-3-031-73497-7
eBook Packages: Computer ScienceComputer Science (R0)