{"url":"/method/acgpn","slug":"acgpn","name":"ACGPN","full_name":"Adaptive Content Generating and Preserving Network","full_name_withheld":false,"description_markdown":"**ACGPN**, or **Adaptive Content Generating and Preserving Network**, is a [generative adversarial network](https://www.paperswithcode.com/method/category/generative-adversarial-network) for virtual try-on clothing applications. \r\n\r\nIn Step I, the Semantic Generation Module (SGM) takes the target clothing image $\\mathcal{T}\\_{c}$, the pose map $\\mathcal{M}\\_{p}$, and the fused body part mask $\\mathcal{M}^{F}$ as the input to predict the semantic layout and to output the synthesized body part mask $\\mathcal{M}^{S}\\_{\\omega}$ and the target clothing mask $\\mathcal{M}^{S\\_{c}$.\r\n\r\nIn Step II, the Clothes Warping Module (CWM) warps the target clothing image to $\\mathcal{T}^{R}\\_{c}$ according to the predicted semantic layout, where a second-order difference constraint is introduced to stabilize the warping process. \r\n\r\nIn Steps III and IV, the Content Fusion Module (CFM) first produces the composited body part mask $\\mathcal{M}^{C}\\_{\\omega}$ using the original clothing mask $\\mathcal{M}\\_{c}$, the synthesized clothing mask $\\mathcal{M}^{S}\\_{c}$, the body part mask $\\mathcal{M}\\_{\\omega}$, and the synthesized body part mask $\\mathcal{M}\\_{\\omega}^{S}$, and then exploits a fusion network to generate the try-on images $\\mathcal{I}^{S}$ by utilizing the information $\\mathcal{T}^{R}\\_{c}$, $\\mathcal{M}^{S}\\_{c}$, and the body part image $I\\_{\\omega}$ from previous steps.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Towards Photo-Realistic Virtual Try-On by Adaptively Generating-Preserving Image Content","paper":"/paper/towards-photo-realistic-virtual-try-on-by-1","first_author":"Han Yang","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/towards-photo-realistic-virtual-try-on-by-1"},"source":{"url":"http://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Towards_Photo-Realistic_Virtual_Try-On_by_Adaptively_Generating-Preserving_Image_Content_CVPR_2020_paper.html","title":"Towards Photo-Realistic Virtual Try-On by Adaptively Generating-Preserving Image Content","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Augmented Reality Methods","url":"/methods/category/augmented-reality-methods","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Adversarial Networks","url":"/methods/category/generative-adversarial-networks","pwc_aliases":[]}],"n_papers_tagged":3,"archive_num_papers":3,"papers_newest_first":[{"paper":"/paper/towards-photo-realistic-virtual-try-on-by-1","title":"Towards Photo-Realistic Virtual Try-On by Adaptively Generating-Preserving Image Content","date":"2020-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/towards-photo-realistic-virtual-try-on-by","title":"Towards Photo-Realistic Virtual Try-On by Adaptively Generating$\\leftrightarrow$Preserving Image Content","date":"2020-03-12","arxiv_id":"2003.05863","n_code_links":3,"syntology":{"ran":1,"of":2,"unverified":1,"pointer_only":2}},{"paper":"/paper/online-cyber-attack-detection-in-smart-grid-a","title":"Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach","date":"2018-09-14","arxiv_id":"1809.05258","n_code_links":1,"syntology":null}],"papers_shown":3,"tasks":[{"task":"/task/layout-generation","name":"Layout Generation","papers":2},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":2},{"task":"/task/virtual-try-on","name":"Virtual Try-on","papers":2},{"task":"/task/anomaly-detection","name":"Anomaly Detection","papers":1},{"task":"/task/cyber-attack-detection","name":"Cyber Attack Detection","papers":1},{"task":"/task/outlier-detection","name":"Outlier Detection","papers":1},{"task":"/task/reinforcement-learning","name":"Reinforcement Learning","papers":1},{"task":"/task/reinforcement-learning-1","name":"Reinforcement Learning (RL)","papers":1},{"task":"/task/reinforcement-learning-2","name":"reinforcement-learning","papers":1}],"tasks_shown":9,"n_tasks":9,"usage_by_year":[{"year":"2018","papers":1},{"year":"2020","papers":2}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/acgpn"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}