Revolutionizing Virtual Try-On: Key Findings and Future Directions

Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Table of Links Abstract and 1. Introduction 2. Related Work 3. Method 3.1. Cascaded Diffusion Models for Try-On 3.2. Parallel-UNet 4. Experiments 5. Summary and Future Work and References Appendix A. Implementation Details B. Additional Results 5. Summary and Future Work We presented a method that allows to synthesize try-on given an image of a person and an image of a garment. Our results are overwhelmingly better than state-of-the-art, both in the quality of the warp to new body shapes and poses, and in the preservation of the garment. Our novel architecture Parallel-UNet, where two UNets are trained in parallel and one UNet sends information to the other via cross attentions, turned out to create state-of-the-art results. In addition to the exciting progress for the specific application of virtual try-on, we believe this architecture is going to be impactful for the general case of image editing, which we are excited to explore in the future. Finally, we believe that the architecture could also be extended to videos, which we also plan to pursue in the future. References [1] Walmart Virtual Try-On. https://www.walmart. com/cp/virtual-try-on/4879497. 2 [2] Shuai Bai, Huiling Zhou, Zhikang Li, Chang Zhou, and Hongxia Yang. Single stage virtual try-on via deformable attention flows. In European Conference on Computer Vision, pages 409–425. Springer, 2022. 2, 5, 6, 11, 12, 13, 14, 15, 16 [3] Mikołaj Binkowski, Danica J Sutherland, Michael Arbel, and ´ Arthur Gretton. Demystifying mmd gans. arXiv preprint arXiv:1801.01401, 2018. 6 [4] James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. JAX: composable transformations of Python+NumPy programs, 2018. 11 [5] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018. 3 [6] Seunghwan Choi, Sunghyun Park, Minsoo Lee, and Jaegul Choo. Viton-hd: High-resolution virtual try-on via misalignment-aware normalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14131–14140, 2021. 2, 4, 5, 7, 17 [7] Xin Dong, Fuwei Zhao, Zhenyu Xie, Xijin Zhang, Daniel K Du, Min Zheng, Xiang Long, Xiaodan Liang, and Jianchao Yang. Dressing in the wild by watching dance videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3480–3489, 2022. 2 [8] Vincent Dumoulin, Ethan Perez, Nathan Schucher, Florian Strub, Harm de Vries, Aaron Courville, and Yoshua Bengio. Feature-wise transformations. Distill, 3(7):e11, 2018. 5, 11 [9] Stefan Elfwing, Eiji Uchibe, and Kenji Doya. Sigmoidweighted linear units for neural network function approximation in reinforcement learning. Neural Networks, 107:3–11, 2018. 11 [10] Yuying Ge, Yibing Song, Ruimao Zhang, Chongjian Ge, Wei Liu, and Ping Luo. Parser-free virtual try-on via distilling appearance flows. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8485–8493, 2021. 2 [11] Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, and Liang Lin. Graphonomy: Universal human parsing via graph transfer learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7450–7459, 2019. 4, 8 [12] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139–144, 2020. 3 [13] Xintong Han, Xiaojun Hu, Weilin Huang, and Matthew R Scott. Clothflow: A flow-based model for clothed person generation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10471–10480, 2019. 2 [14] Xintong Han, Zuxuan Wu, Zhe Wu, Ruichi Yu, and Larry S Davis. Viton: An image-based virtual try-on network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7543–7552, 2018. 2 [15] Sen He, Yi-Zhe Song, and Tao Xiang. Style-based global appearance flow for virtual try-on. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3470–3479, June 2022. 2 [16] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 6 [17] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840–6851, 2020. 3, 4 [18] Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. J. Mach. Learn. Res., 23:47–1, 2022. 4 [19] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 5 [20] Thibaut Issenhuth, Jer´ emie Mary, and Cl ´ ement Calauz ´ enes. ` Do not mask what you do not need to mask: a parser-free virtual try-on. In European Conference on Computer Vision, pages 619–635. Springer, 2020. 2 [21] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In European conference on computer vision, pages 694–711. Springer, 2016. 2 [22] Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Training generative adversarial networks with limited data. Advances in Neural Information Processing Systems, 33:12104–12114, 2020. 5, 12 [23] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110–8119, 2020. 2 [24] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 5 [25] Sangyun Lee, Gyojung Gu, Sunghyun Park, Seunghwan Choi, and Jaegul Choo. High-resolution virtual try-on with misalignment and occlusion-handled conditions. In Proceedings of the European conference on computer vision (ECCV), 2022. 2, 5, 6, 11, 13, 14, 15, 16 [26] Kathleen M Lewis, Srivatsan Varadharajan, and Ira Kemelmacher-Shlizerman. Tryongan: Body-aware try-on via layered interpolation. ACM Transactions on Graphics (TOG), 40(4):1–10, 2021. 2, 5, 6, 13, 14, 15, 16 [27] Yifang Men, Yiming Mao, Yuning Jiang, Wei-Ying Ma, and Zhouhui Lian. Controllable person image synthesis with attribute-decomposed gan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5084–5093, 2020. 2 [28] George Papandreou, Tyler Zhu, Nori Kanazawa, Alexander Toshev, Jonathan Tompson, Chris Bregler, and Kevin Murphy. Towards accurate multi-person pose estimation in the 9 wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4903–4911, 2017. 4, 8 [29] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748–8763. PMLR, 2021. 5 [30] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022. 3 [31] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Curless. Film: Frame interpolation for large motion. In The European Conference on Computer Vision (ECCV), 2022. 2 [32] Yurui Ren, Xiaoqing Fan, Ge Li, Shan Liu, and Thomas H Li. Neural texture extraction and distribution for controllable person image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13535–13544, 2022. 2 [33] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer. High-resolution image ¨ synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684–10695, 2022. 3 [34] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. Unet: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. 3, 4 [35] Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. arXiv preprint arXiv:2208.12242, 2022. 3 [36] Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1– 10, 2022. 3, 4 [37] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 2022. 2, 3, 4, 5 [38] Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. Image superresolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 3, 4 [39] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256–2265. PMLR, 2015. 3, 4 [40] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 4 [41] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems, 32, 2019. 3 [42] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2, 4 [43] Bochao Wang, Huabin Zheng, Xiaodan Liang, Yimin Chen, Liang Lin, and Meng Yang. Toward characteristicpreserving image-based virtual try-on network. In Proceedings of the European conference on computer vision (ECCV), pages 589–604, 2018. 2 [44] Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel view synthesis with diffusion models. arXiv preprint arXiv:2210.04628, 2022. 3 [45] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3–19, 2018. 11 [46] Han Yang, Xinrui Yu, and Ziwei Liu. Full-range virtual try-on with recurrent tri-level transform. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3460–3469, June 2022. 2 [47] Han Yang, Ruimao Zhang, Xiaobao Guo, Wei Liu, Wangmeng Zuo, and Ping Luo. 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, pages 7850–7859, 2020. 2 [48] Ruiyun Yu, Xiaoqi Wang, and Xiaohui Xie. Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10511– 10520, 2019. 2 [49] Jinsong Zhang, Kun Li, Yu-Kun Lai, and Jingyu Yang. Pise: Person image synthesis and editing with decoupled gan. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7982–7990, 2021. 2 [50] Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159, 2020. 2 This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Authors: Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. Method 3. Method 3.1. Cascaded Diffusion Models for Try-On 3.1. Cascaded Diffusion Models for Try-On 3.2. Parallel-UNet 3.2. Parallel-UNet 4. Experiments 4. Experiments 5. Summary and Future Work and References 5. Summary and Future Work and References Appendix Appendix A. Implementation Details A. Implementation Details B. Additional Results B. Additional Results 5. Summary and Future Work We presented a method that allows to synthesize try-on given an image of a person and an image of a garment. Our results are overwhelmingly better than state-of-the-art, both in the quality of the warp to new body shapes and poses, and in the preservation of the garment. Our novel architecture Parallel-UNet, where two UNets are trained in parallel and one UNet sends information to the other via cross attentions, turned out to create state-of-the-art results. In addition to the exciting progress for the specific application of virtual try-on, we believe this architecture is going to be impactful for the general case of image editing, which we are excited to explore in the future. Finally, we believe that the architecture could also be extended to videos, which we also plan to pursue in the future. References [1] Walmart Virtual Try-On. https://www.walmart. com/cp/virtual-try-on/4879497. 2 [2] Shuai Bai, Huiling Zhou, Zhikang Li, Chang Zhou, and Hongxia Yang. Single stage virtual try-on via deformable attention flows. In European Conference on Computer Vision, pages 409–425. Springer, 2022. 2, 5, 6, 11, 12, 13, 14, 15, 16 [3] Mikołaj Binkowski, Danica J Sutherland, Michael Arbel, and ´ Arthur Gretton. Demystifying mmd gans. arXiv preprint arXiv:1801.01401, 2018. 6 [4] James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. JAX: composable transformations of Python+NumPy programs, 2018. 11 [5] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018. 3 [6] Seunghwan Choi, Sunghyun Park, Minsoo Lee, and Jaegul Choo. Viton-hd: High-resolution virtual try-on via misalignment-aware normalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14131–14140, 2021. 2, 4, 5, 7, 17 [7] Xin Dong, Fuwei Zhao, Zhenyu Xie, Xijin Zhang, Daniel K Du, Min Zheng, Xiang Long, Xiaodan Liang, and Jianchao Yang. Dressing in the wild by watching dance videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3480–3489, 2022. 2 [8] Vincent Dumoulin, Ethan Perez, Nathan Schucher, Florian Strub, Harm de Vries, Aaron Courville, and Yoshua Bengio. Feature-wise transformations. Distill, 3(7):e11, 2018. 5, 11 [9] Stefan Elfwing, Eiji Uchibe, and Kenji Doya. Sigmoidweighted linear units for neural network function approximation in reinforcement learning. Neural Networks, 107:3–11, 2018. 11 [10] Yuying Ge, Yibing Song, Ruimao Zhang, Chongjian Ge, Wei Liu, and Ping Luo. Parser-free virtual try-on via distilling appearance flows. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8485–8493, 2021. 2 [11] Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, and Liang Lin. Graphonomy: Universal human parsing via graph transfer learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7450–7459, 2019. 4, 8 [12] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139–144, 2020. 3 [13] Xintong Han, Xiaojun Hu, Weilin Huang, and Matthew R Scott. Clothflow: A flow-based model for clothed person generation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10471–10480, 2019. 2 [14] Xintong Han, Zuxuan Wu, Zhe Wu, Ruichi Yu, and Larry S Davis. Viton: An image-based virtual try-on network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7543–7552, 2018. 2 [15] Sen He, Yi-Zhe Song, and Tao Xiang. Style-based global appearance flow for virtual try-on. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3470–3479, June 2022. 2 [16] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017. 6 [17] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840–6851, 2020. 3, 4 [18] Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. J. Mach. Learn. Res., 23:47–1, 2022. 4 [19] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 5 [20] Thibaut Issenhuth, Jer´ emie Mary, and Cl ´ ement Calauz ´ enes. ` Do not mask what you do not need to mask: a parser-free virtual try-on. In European Conference on Computer Vision, pages 619–635. Springer, 2020. 2 [21] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In European conference on computer vision, pages 694–711. Springer, 2016. 2 [22] Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Training generative adversarial networks with limited data. Advances in Neural Information Processing Systems, 33:12104–12114, 2020. 5, 12 [23] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110–8119, 2020. 2 [24] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 5 [25] Sangyun Lee, Gyojung Gu, Sunghyun Park, Seunghwan Choi, and Jaegul Choo. High-resolution virtual try-on with misalignment and occlusion-handled conditions. In Proceedings of the European conference on computer vision (ECCV), 2022. 2, 5, 6, 11, 13, 14, 15, 16 [26] Kathleen M Lewis, Srivatsan Varadharajan, and Ira Kemelmacher-Shlizerman. Tryongan: Body-aware try-on via layered interpolation. ACM Transactions on Graphics (TOG), 40(4):1–10, 2021. 2, 5, 6, 13, 14, 15, 16 [27] Yifang Men, Yiming Mao, Yuning Jiang, Wei-Ying Ma, and Zhouhui Lian. Controllable person image synthesis with attribute-decomposed gan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5084–5093, 2020. 2 [28] George Papandreou, Tyler Zhu, Nori Kanazawa, Alexander Toshev, Jonathan Tompson, Chris Bregler, and Kevin Murphy. Towards accurate multi-person pose estimation in the 9 wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4903–4911, 2017. 4, 8 [29] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748–8763. PMLR, 2021. 5 [30] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022. 3 [31] Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, and Brian Curless. Film: Frame interpolation for large motion. In The European Conference on Computer Vision (ECCV), 2022. 2 [32] Yurui Ren, Xiaoqing Fan, Ge Li, Shan Liu, and Thomas H Li. Neural texture extraction and distribution for controllable person image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13535–13544, 2022. 2 [33] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer. High-resolution image ¨ synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684–10695, 2022. 3 [34] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. Unet: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. 3, 4 [35] Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. arXiv preprint arXiv:2208.12242, 2022. 3 [36] Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1– 10, 2022. 3, 4 [37] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 2022. 2, 3, 4, 5 [38] Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. Image superresolution via iterative refinement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 3, 4 [39] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256–2265. PMLR, 2015. 3, 4 [40] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 4 [41] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems, 32, 2019. 3 [42] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2, 4 [43] Bochao Wang, Huabin Zheng, Xiaodan Liang, Yimin Chen, Liang Lin, and Meng Yang. Toward characteristicpreserving image-based virtual try-on network. In Proceedings of the European conference on computer vision (ECCV), pages 589–604, 2018. 2 [44] Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel view synthesis with diffusion models. arXiv preprint arXiv:2210.04628, 2022. 3 [45] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3–19, 2018. 11 [46] Han Yang, Xinrui Yu, and Ziwei Liu. Full-range virtual try-on with recurrent tri-level transform. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3460–3469, June 2022. 2 [47] Han Yang, Ruimao Zhang, Xiaobao Guo, Wei Liu, Wangmeng Zuo, and Ping Luo. 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, pages 7850–7859, 2020. 2 [48] Ruiyun Yu, Xiaoqi Wang, and Xiaohui Xie. Vtnfp: An image-based virtual try-on network with body and clothing feature preservation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10511– 10520, 2019. 2 [49] Jinsong Zhang, Kun Li, Yu-Kun Lai, and Jingyu Yang. Pise: Person image synthesis and editing with decoupled gan. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7982–7990, 2021. 2 [50] Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159, 2020. 2 This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv