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This paper introduces a novel framework for virtual try-on, termed Wear-Any-Way.
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2013
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Wang, B., Zheng, H., Liang, X., Chen, Y., Lin, L., Yang, M.: Toward characteristic-preserving image-based virtual try-on network. In: ECCV. pp. 589–604 (2018)
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Han, X., Hu, X., Huang, W., Scott, M.R.: Clothflow: A flow-based model for clothed person generation. In: ICCV. pp. 10471–10480 (2019)
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Jo, Y., Park, J.: Sc-fegan: Face editing generative adversarial network with user’s sketch and color. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 1745–1753 (2019)
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Liu, L., Zhang, H., Ji, Y., Wu, Q.J.: Toward ai fashion design: An attribute-gan model for clothing match. Neurocomputing 341
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Zou, X., Kong, X., Wong, W., Wang, C., Liu, Y., Cao, Y.: Fashionai: A hierarchical dataset for fashion understanding. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 0–0 (2019)
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Dong, H., Liang, X., Zhang, Y., Zhang, X., Shen, X., Xie, Z., Wu, B., Yin, J.: Fashion editing with adversarial parsing learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8120–8128 (2020)
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. NeurIPS (2020)
2020
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
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Issenhuth, T., Mary, J., Calauzenes, C.: Do not mask what you do not need to mask: a parser-free virtual try-on. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XX 16. pp. 619–635. Springer (2020)
2020
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Lee, C.H., Liu, Z., Wu, L., Luo, P.: Maskgan: Towards diverse and interactive facial image manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5549–5558 (2020)
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Liu, J., Song, X., Chen, Z., Ma, J.: Mgcm: Multi-modal generative compatibility modeling for clothing matching. Neurocomputing 414
2020
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Sarlin, P.E., DeTone, D., Malisiewicz, T., Rabinovich, A.: Superglue: Learning feature matching with graph neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4938–4947 (2020)
2020
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Choi, S., Park, S., Lee, M., Choo, J.: Viton-hd: High-resolution virtual try-on via misalignment-aware normalization. In: CVPR. pp. 14131–14140 (2021)
2021
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Choi, S., Park, S., Lee, M., Choo, J.: Viton-hd: High-resolution virtual try-on via misalignment-aware normalization. In: CVPR (2021)
2021
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Ge, Y., Song, Y., Zhang, R., Ge, C., Liu, W., Luo, P.: Parser-free virtual try-on via distilling appearance flows. In: CVPR. pp. 8485–8493 (2021)
2021
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Liu, X., Li, J., Wang, J., Liu, Z.: Mmfashion: An open-source toolbox for visual fashion analysis. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 3755–3758 (2021)
2021
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2021
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)
2021
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Avrahami, O., Lischinski, D., Fried, O.: Blended diffusion for text-driven editing of natural images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18208–18218 (2022)
2022
Cited alongside, same era.
Bai, S., Zhou, H., Li, Z., Zhou, C., Yang, H.: Single stage virtual try-on via deformable attention flows. In: European Conference on Computer Vision. pp. 409–425. Springer (2022)
2022
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2022
Cited alongside, same era.
2022
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2023
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2023
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2023
Later among the works it cites.
2023
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2022
Cited alongside, same era.
Lee, S., Gu, G., Park, S., Choi, S., Choo, J.: High-resolution virtual try-on with misalignment and occlusion-handled conditions. In: ECCV. pp. 204–219. Springer (2022)
2022
Cited alongside, same era.
Morelli, D., Fincato, M., Cornia, M., Landi, F., Cesari, F., Cucchiara, R.: Dress code: High-resolution multi-category virtual try-on. In: ECCV. pp. 2231–2235 (2022)
2022
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR (2022)
2022
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)
2022
Cited alongside, same era.
Wang, S.Y., Bau, D., Zhu, J.Y.: Rewriting geometric rules of a gan. ACM Transactions on Graphics (TOG) 41
2022
Cited alongside, same era.
Bhunia, A.K., Khan, S., Cholakkal, H., Anwer, R.M., Laaksonen, J., Shah, M., Khan, F.S.: Person image synthesis via denoising diffusion model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5968–5976 (2023)
2023
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Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: Learning to follow image editing instructions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18392–18402 (2023)
2023
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2023
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2023
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Pan, X., Tewari, A., Leimkühler, T., Liu, L., Meka, A., Theobalt, C.: Drag your gan: Interactive point-based manipulation on the generative image manifold. In: ACM SIGGRAPH 2023 Conference Proceedings. pp. 1–11 (2023)
2023
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Pautrat, R., Suárez, I., Yu, Y., Pollefeys, M., Larsson, V.: Gluestick: Robust image matching by sticking points and lines together. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9706–9716 (2023)
2023
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Valevski, D., Kalman, M., Molad, E., Segalis, E., Matias, Y., Leviathan, Y.: Unitune: Text-driven image editing by fine tuning a diffusion model on a single image. ACM Transactions on Graphics (TOG) 42
2023
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Xie, Z., Huang, Z., Dong, X., Zhao, F., Dong, H., Zhang, X., Zhu, F., Liang, X.: Gp-vton: Towards general purpose virtual try-on via collaborative local-flow global-parsing learning. In: CVPR. pp. 23550–23559 (2023)
2023
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2023
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Yang, B., Gu, S., Zhang, B., Zhang, T., Chen, X., Sun, X., Chen, D., Wen, F.: Paint by example: Exemplar-based image editing with diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18381–18391 (2023)
2023
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Yang, Z., Zeng, A., Yuan, C., Li, Y.: Effective whole-body pose estimation with two-stages distillation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4210–4220 (2023)
2023
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Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3836–3847 (2023)
2023
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Zhu, L., Yang, D., Zhu, T., Reda, F., Chan, W., Saharia, C., Norouzi, M., Kemelmacher-Shlizerman, I.: Tryondiffusion: A tale of two unets. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4606–4615 (2023)
2023
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Hedlin, E., Sharma, G., Mahajan, S., Isack, H., Kar, A., Tagliasacchi, A., Yi, K.M.: Unsupervised semantic correspondence using stable diffusion. Advances in Neural Information Processing Systems 36
2024
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Tang, L., Jia, M., Wang, Q., Phoo, C.P., Hariharan, B.: Emergent correspondence from image diffusion. Advances in Neural Information Processing Systems 36
2024
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Zhang, J., Herrmann, C., Hur, J., Polania Cabrera, L., Jampani, V., Sun, D., Yang, M.H.: A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence. Advances in Neural Information Processing Systems 36
2024
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