Deep Learning-Driven Virtual Clothing Fit Detection and Automatic Pattern Correction Technology
Main Article Content
Abstract
Online fashion platforms continue to experience significant refund rates due to inaccurate virtual fit assessments and the lack of integrated, automatic Fabric Pattern Adjustment. Existing virtual try-on methods typically rely on 2D image alignment and mesh-based simulations, which provide poor collision awareness and inefficient large-scale deformation modelling. To solve the above issue, this Work introduces a unified Deep Learning Framework (SDF-FNO) that combines a Signed Distance Function (SDF)-based neural network for virtual clothing fit identification and a Fourier Neural Operator (FNO) for automatic garment pattern correction. The SDF network uses collision-aware surface evaluation to determine tight, normal, and loose fit zones by modelling the human body and garment as continuous implicit surfaces and computing signed distance differences. The FNO learns to modify the entire garment pattern at once, then reconstructs a smooth, corrected pattern by analysing global shape changes in the frequency domain. The system is trained and tested using the 4D-DRESS dataset, which contains dynamic 3D clothed human scans. The SDF-FNO demonstrates scalability across e-commerce virtual try-on, augmented-reality retail, digital tailoring, and intelligent clothing production systems. The experimental results show 94.6% fit classification accuracy, 93.9% automated pattern correction success rate, and 94.3% overall application-level accuracy, while maintaining stable real-time inference performance.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
H. Zhu, Y. Cao, H. Jin, W. Chen, D. Du, Z. Wang, et al., “DeepFashion3D: A dataset and benchmark for 3D garment reconstruction from single images,” in Vedaldi A, Bischof H, Brox T, Frahm JM, editors. European Conference on Computer Vision (ECCV 2020). Proceedings of the European Conference on Computer Vision, 2020. Cham: Springer International Publishing, 2020, pp. 512–530, doi: 10.1007/978-3-030-58452-8_30.
Li R, Cao C, Dumery C, You Y, Li H, Fua P. Single view garment reconstruction using diffusion mapping via pattern coordinates, “In: Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers; 2025 Aug,” New York: ACM; 2025. p. 1–11, doi: 10.1145/3721238.3730651.
Li X, Yu C, Du W, et al. Dress-1-to-3: Single image to simulation-ready 3D outfit with diffusion prior and differentiable physics, “ACM Transactions on Graphics (TOG),” 2025;44(4):1–16, doi: 10.1145/3731177.
Guo J, Chen J, Chen W, et al. GarmentX: Autoregressive Parametric Representations for High Fidelity 3D Garment Generation, “arXiv preprint,” 2025, doi: 10.48550/arXiv.2504.20409.
C. Zhang, Y. Wang, F. V. Carrasco, et al., “Fabric Diffusion: High Fidelity Texture Transfer for 3D Garments Generation from In The Wild Clothing Images,” arXiv preprint, 2024, doi: 10.1145/3680528.3687637.
S. K. A. Narasimhan, P. Kanakaraj, and R. Rajaopalan, “Virtual fit evaluation for optimization of garment component’s parameters,” Tekstil ve Muhendis, vol. 31, no. 136, pp. 269–276, 2024, doi: 10.7216/teksmuh.1500589.
C. Chen, J. Ni, and P. Zhang, “Virtual try-on systems in fashion consumption: A systematic review,” Applied Sciences, vol. 14, no. 24, Art. no. 11839, 2024, doi: 10.3390/app142411839.
L. Bai, C. Tao, J. Chen, et al., “Modeling of virtual clothing and its contact with the human body,” AUTEX Research Journal, vol. 24, no. 1, Art. no. 20230039, 2024.
J. Lee, H. Jung, and J. Choi, “3D-aware virtual try-on using only 2D inputs,” Computer Vision and Image Understanding, Art. no. 104661, 2026, doi: 10.1016/j.cviu.2026.104661.
Y. Cho, S. Ray L S, P. Thota K S, et al., “Clothfit: Cloth-human-attribute guided virtual try-on network using 3D simulated dataset,” in 2023 IEEE International Conference on Image Processing (ICIP), 2023. IEEE, 2023, pp. 3484–3488, doi: 10.1109/ICIP49359.2023.10222494.
V. B. Aakash, A. Akmal, A. Chandrababu, et al., “A comprehensive survey on AI-driven fashion technologies: Clothing detection, recommendation systems, and virtual try-on solutions,” International Journal of Advances in Engineering and Management, vol. 6, no. 11, pp. 247–252, 2024, doi: 10.35629/5252-0611247252.
T. Islam, A. Miron, X. Liu, et al., “Deep learning in virtual try-on: A comprehensive survey,” IEEE Access, vol. 12, pp. 29475– 29502, 2024, doi: 10.1109/ACCESS.2024.3368612.
M. Servi, R. Magherini, F. Buonamici, et al., “Integration of artificial intelligence and augmented reality for assisted detection of textile defects,” Journal of Engineered Fibers and Fabrics, vol. 19, Art. no. 15589250231206502, 2024, doi: 10.1177/15589250231206502.
S. Mei, Y. Wang, and G. Wen, “Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model,” Sensors, vol. 18, no. 4, pp. 1064, 2018, doi: 10.3390/s18041064.
C. F. J. Kuo, W. R. Wang, and J. Barman, “Automated optical inspection for defect identification and classification in actual woven fabric production lines,” Sensors, vol. 22, no. 19, pp. 7246, 2022, doi: 10.3390/s22197246.
R. Carrilho, K. A. Hambarde, and H. Proenca, “A novel dataset for fabric defect detection: bridging gaps in anomaly detection,” Applied Sciences, vol. 14, no. 12, pp. 5298, 2024, doi: 10.3390/app14125298.
K. H. Foysal, et al., “SmartFit: Smartphone application for garment fit detection,” Electronics, vol. 10, no. 1, pp. 97, 2021, doi: 10.3390/electronics10010097.
J. Xiang, R. Pan, and W. Gao, “Online detection of fabric defects based on improved centernet with deformable convolution,” Sensors, vol. 22, no. 13, pp. 4718, 2022, doi: 10.3390/s22134718.
H. Sun, J. Yao, H. Zhang, et al., “A digital simulation and Re-Editing method for clothing patterns based on deep learning and somatosensory interaction,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 37, no. 11, Art. no. 2352016, 2023, doi: 10.1142/S021800142352016X.
Y. Gao, “A design of apparel appearance: Recognition and evaluation of clothing pattern styles under deep learning,” The Leather and Textile Research Journal, vol. 110, pp. 1-12, 2023, doi: 10.31881/TLR.2023.110.
J. Dustin, “Advancements in virtual garment simulation and digital fit analysis for custom fashion design,” International Journal of Fashion Design Research and Development (IJFDRD), vol. 2, no. 1, pp. 1–7, 2025.
K. Liu, X. Zeng, P. Bruniaux, et al., “Fit evaluation of virtual garment try-on by learning from digital pressure data,” Knowledge-Based Systems, vol. 133, pp. 174–182, 2017, doi: 10.1016/j.knosys.2017.07.007.
M. M. M. Nayak, M. N. Rani, K. Vaibhavi, M. Ashwinishree, and K. M. Suchithra, “AI fit-virtual clothing try on system using deep learning,” IJCSE, vol. 9, no. 6, pp. 378–381, 2025.
I. Kachbal and S. El Abdellaoui, “Computer vision for fashion: A systematic review of design generation, simulation, and personalized recommendations,” Information, vol. 17, no. 1, pp. 11, 2025, doi: 10.3390/info17010011.
H. Xu, J. Li, G. Lu, et al., “Predicting ready-made garment dressing fit for individuals based on highly reliable examples,” Computers & Graphics, vol. 90, pp. 135–144, 2020, doi: 10.1016/j.cag.2020.06.002.
W. Wang, H. I. Ho, C. Guo, et al., “4d-dress: A 4d dataset of real-world human clothing with semantic annotations,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 550-560, 2024, doi: 10.1109/CVPR52733.2024.00059.