Abstract
Virtual try-on based on image synthesis aims to combine the customer’s photo with in-shop clothes to acquire a try-on image. The key to generating a realistic try-on image is whether the in-shop clothes is spatially aligned with the customer’s body. Prior methods usually directly adopt the spatial transformation network to complete the clothing deformation, but they cannot generate high-quality try-on images when facing the customer’s posture changes or complex clothes pattern. To address it, we propose a virtual try-on network based on landmark constraint (LCVTON) in this work. Specifically, we notice the corresponding relationship between the clothes feature points and the customer body feature points, making the clothes match more closely with the customer’s body. The matching of the feature points enables us to introduce the landmark constraint into the spatial transformer network for naturally and smoothly warping clothes. Moreover, we construct a refinement network and introduce the landmark constraint into it to preserve the texture details of clothes. We conducted experiments on the try-on dataset and compared our method with existing methods. Both qualitative and quantitative results demonstrate the superiority of our method compared to existing state-of-the-art method.
This work was supported in part by the National Natural Science Foundation of China under Grant 61671480, in part by the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008, in part by the Natural Science Foundation of Shandong Province under Grant ZR2019MF073, and in part by the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China) under Grant 20CX05001A.
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References
Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: toward characteristic-preserving image-based virtual try-on network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 589–604 (2018)
Yang, H., Zhang, R., Guo, X., Liu, W., Zuo, W., Luo, P.: Towards photo-realistic virtual try-on by adaptively generating-preserving image content. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7850–59 (2020)
Ge, Y., Song, Y., Zhang, R., Ge, C., Liu, W., Luo, P.: Parser-free virtual try-on via distilling appearance flows. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8485–93 (2021)
Ge, C., Song, Y., Ge, Y., Yang, H., Liu, W., Luo, P.: Disentangled cycle consistency for highly-realistic virtual try-on. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16928–16937 (2021)
He, S., Song, Y.Z., Xiang, T.: Style-based global appearance flow for virtual try-on. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3470–3479 (2022)
Han, X., Wu, Z., Wu, Z., Yu, R., Davis, L.S.: VITON: an image-based virtual try-on network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7543–7552 (2018)
Song, D., Li, T., Mao, Z., Liu, A.-A.: SP-VITON: shape-preserving image-based virtual try-on network. Multimedia Tools Appl. 79(45), 33757–33769 (2020). https://doi.org/10.1007/s11042-019-08363-w
Issenhuth, T., Mary, J., Calauzènes, C.: Do not mask what you do not need to mask: a parser-free virtual try-on. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 619–635. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58565-5_37
Duchon, J.: Splines minimizing rotation-invariant semi-norms in Sobolev spaces. In: Schempp, W., Zeller, K. (eds.) Constructive Theory of Functions of Several Variables. LNM, vol. 571, pp. 85–100. Springer, Heidelberg (1977). https://doi.org/10.1007/BFb0086566
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Karnewar, A., Wang, O.: MSG-GAN: multi-scale gradients for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7799–7808 (2020)
Hussein, S.A., Tirer, T., Giryes, R.: Image-adaptive GAN based reconstruction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3121–3129 (2020)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=Hk99zCeAb
Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. In: NIPS Workshop on Adversarial Training. Barcelona, Spain (2016). https://hal.inria.fr/hal-01398049
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8798–8807 (2018)
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–19 (2020)
Shaham, T.R., Dekel, T., Michaeli, T.: Singan: learning a generative model from a single natural image. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4570–80 (2019)
Abdal, R., Zhu, P., Mitra, N.J., Wonka, P.: Styleflow: attribute-conditioned exploration of styleGAN-generated images using conditional continuous normalizing flows. ACM Trans. Graph. (TOG) 40(3), 1–21 (2021)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)
Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693–5703 (2019)
Lin, T.H.: Aggregation and Finetuning for Clothes Landmark Detection (2020)
Gong, K., Liang, X., Zhang, D., Shen, X., Lin, L.: Look into person: self-supervised structure-sensitive learning and a new benchmark for human parsing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 932–940 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press, Cambridge (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ArXiv Preprint ArXiv:1409.1556 (2014)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Ge, Y., Zhang, R., Wang, X., Tang, X., Luo, P.: Deepfashion2: a versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5337–5345 (2019)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–6112 (2004)
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Wu, H., Yao, X., Liu, B., Lu, X., Liu, W. (2023). Virtual Try-on via Matching Relation with Landmark. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_5
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