Abstract
This work introduces a novel method for the creation of an in-home virtual dressing room for garment fitting using an integrated system consisting of personalized 3D body model reconstruction and garment fitting simulation. Our method gives saliency to and establishes a relational interconnection between the massive scatter points on the 3D generic model. Starting with a small set of anthropometric interconnected ordered intrinsic control points residing on the silhouette of the projections of the generic model, corresponding control points on two canonical images of the person are automatically found – hence importing equivalent saliency between the two sets. Further equivalent saliencies between the projected points from the generic model and the canonical images are established through a loop subdivision process. Human shape mesh personalization is done through morphing the points on the generic model to follow and be consistent with their equivalent points on the canonical images. The 3D reconstruction yields sub resolution errors (high level accuracy) when compared to the average resolution of the original model using the CAESAR dataset. The reconstructed model is then fitted with garments sized to the 3D personalized model given at least one frontal image of the garment with no requirement for a full 3D view of the garment. Our method can also be applied to virtual fitting system for online stores and/or for clothing design and personalized garment simulation. The method is convenient, simple, efficient, and requires no intervention from the user aside from taking two images with a camera or smart phone.
Similar content being viewed by others
Data Availability
Statistical human shape of CAESAR dataset is downloaded from http://humanshape.mpi-inf.mpg.de/#download.
References
Amenta N, Bern M, Kamvysselis M (1998) A new Voronoi-based surface reconstruction algorithm. Proceedings of the 25th annual conference on Computer graphics and interactive techniques pp 415–421.
Anguelov D, Srinivasan P, Koller D, Thrun S, Rodgers J, Davis J (2005) SCAPE: shape completion and animation of people. ACM SIGGRAPH 24:408–416
Boisvert J, Shu C, Wuhrer S, Xi P (2013) Three-dimensional human shape inference from silhouettes: reconstruction and validation. Mach Vis Appl 24(1):145–157
Chen Y, Kim T-K, Cipolla R (2010) Inferring 3D shapes and deformations from single views. European Conference on Computer Vision pp 300–313
Chen W, Wang H, Li Y, Su H, Wang Z, Tu C, Lischinski D, Cohen-Or D, Chen B (2016) Synthesizing training images for boosting human 3d pose estimation. 2016 Fourth International Conference on 3D Vision (3DV) pp 479–488
Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59
Dibra E, Jain H, Öztireli C, Ziegler R, Gross M (2016) Hs-nets: estimating human body shape from silhouettes with convolutional neural networks. 2016 fourth international conference on 3D vision (3DV) pp 108–117
Duan L, Yueqi Z, Ge W, Pengpeng H (2019) Automatic three-dimensional-scanned garment fitting based on virtual tailoring and geometric sewing. J Eng Fibers Fabrics 14:1558925018825319
Guan P, Weiss A, Balan AO, Black MJ (2009) Estimating human shape and pose from a single image. IEEE 12th International Conference on Computer Vision pp 1381–1388
Han X, Wong K-YK YY (2016) 3D human model reconstruction from sparse uncalibrated views. IEEE Comput Graph Appl 36(6):46–56
Hasler N, Stoll C, Sunkel M, Rosenhahn B, Seidel HP (2009) A statistical model of human pose and body shape. Comp Graphics Forum 28:337–346
Hauswiesner S, Straka M, Reitmayr G (2011) Free viewpoint virtual try-on with commodity depth cameras. Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry pp 23–30
Hauswiesner S, Straka M, Reitmayr G (2013) Virtual try-on through image-based rendering. IEEE Trans Vis Comput Graph 19(9):1552–1565
Hilsmann A, Eisert P (2009) Tracking and retexturing cloth for real-time virtual clothing applications. International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications pp 94–105
Huang L, Yang R (2016) Automatic alignment for virtual fitting using 3D garment stretching and human body relocation. Vis Comput 32(6–8):705–715
Jain A, Thormählen T, Seidel H-P, Theobalt C (2010) Moviereshape: tracking and reshaping of humans in videos. In: ACM Transactions on Graphics (TOG) pp 1–10
Johnson S, Everingham M (2010) Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation. bmvc 4:5
Li J, Lu G, Liu Z, Liu J, Wang X (2013) Feature curve-net-based three-dimensional garment customization. Text Res J 83(5):519–531
Loop CT (1987) Smooth subdivision surfaces based on triangles. Master's Thesis, University of Utah
Lunscher N, Zelek J (2018) Deep learning whole body point cloud scans from a single depth map. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops pp 1095–1102
Magnenat-Thalmann N, Seo H, Cordier F (2004) Automatic modeling of virtual humans and body clothing. J Comput Sci Technol 19(5):575–584
Michael N, Drakou M, Lanitis A (2017) Model-based generation of personalized full-body 3D avatars from uncalibrated multi-view photographs. Multimed Tools Appl 76(12):14169–14195
Pishchulin L, Wuhrer S, Helten T, Theobalt C, Schiele B (2017) Building statistical shape spaces for 3d human modeling. Pattern Recogn 67:276–286
Remondino F (2002) Human body reconstruction from image sequences. Joint Pattern Recognition Symposium pp 50–57
Robinette KM, Daanen H, Paquet E (1999) The CAESAR project: a 3-D surface anthropometry survey. Second International Conference on 3-D Digital Imaging and Modeling pp 380–386
Sajjad M, Zahir S, Ullah A, Akhtar Z, Muhammad K (2019) Human behavior understanding in big multimedia data using CNN based facial expression recognition. Mobile networks and applications pp 1–11
Sapp B, Taskar B (2013) Modec: multimodal decomposable models for human pose estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp 3674–3681
Spanlang B, Vassilev T, Buxton BF (2004) Compositing photographs with virtual clothes for design. International Conference on Computer Systems and Technologies pp 1–6
Ul Haq I, Ullah A, Muhammad K, Lee MY, Baik SW (2019) Personalized movie summarization using deep cnn-assisted facial expression recognition. Complexity 2019
Volino P, Magnenat-Thalmann N (2005) Accurate garment prototyping and simulation. Computer-Aided Design Appl 2(5):645–654
Wang D, Sheng Y, Zhang G (2019) A new female body segmentation and feature localisation method for image-based anthropometry. International Conference on Multimedia Modeling pp 567–577
Xi P, Lee W-S, Shu C (2007) A data-driven approach to human-body cloning using a segmented body database. 15th Pacific Conference on Computer Graphics and Applications pp 139–147
Yuan M, Khan IR, Farbiz F, Yao S, Niswar A, Foo M-H (2013) A mixed reality virtual clothes try-on system. IEEE Trans Multimed 15(8):1958–1968
Zhong Y, Xu B (2009) Three-dimensional garment dressing simulation. Text Res J 79(9):792–803
Zhou S, Fu H, Liu L, Cohen-Or D, Han X (2010) Parametric reshaping of human bodies in images. ACM Trans Graphics (TOG) 29(4):126
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Method pipeline design, data collection and experiments analysis, first draft of the manuscript are finished by Chenxi Li; Method optimization suggestions and draft editing are given by Fernand Cohen. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
Not Applicable.
Code availability
Not Applicable.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, C., Cohen, F. In-home application (App) for 3D virtual garment fitting dressing room. Multimed Tools Appl 80, 5203–5224 (2021). https://doi.org/10.1007/s11042-020-09989-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09989-x