{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pp-matting-high-accuracy-natural-image","title":"PP-Matting: High-Accuracy Natural Image Matting","arxiv_id":"2204.09433","date":"2022-04-20","proceeding":null,"authors":["Guowei Chen","Yi Liu","Jian Wang","Juncai Peng","Yuying Hao","Lutao Chu","Shiyu Tang","Zewu Wu","Zeyu Chen","Zhiliang Yu","Yuning Du","Qingqing Dang","Xiaoguang Hu","dianhai yu"],"abstract":"Natural image matting is a fundamental and challenging computer vision task. It has many applications in image editing and composition. Recently, deep learning-based approaches have achieved great improvements in image matting. However, most of them require a user-supplied trimap as an auxiliary input, which limits the matting applications in the real world. Although some trimap-free approaches have been proposed, the matting quality is still unsatisfactory compared to trimap-based ones. Without the trimap guidance, the matting models suffer from foreground-background ambiguity easily, and also generate blurry details in the transition area. In this work, we propose PP-Matting, a trimap-free architecture that can achieve high-accuracy natural image matting. Our method applies a high-resolution detail branch (HRDB) that extracts fine-grained details of the foreground with keeping feature resolution unchanged. Also, we propose a semantic context branch (SCB) that adopts a semantic segmentation subtask. It prevents the detail prediction from local ambiguity caused by semantic context missing. In addition, we conduct extensive experiments on two well-known benchmarks: Composition-1k and Distinctions-646. The results demonstrate the superiority of PP-Matting over previous methods. Furthermore, we provide a qualitative evaluation of our method on human matting which shows its outstanding performance in the practical application. The code and pre-trained models will be available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.","url_abs":"https://arxiv.org/abs/2204.09433v1","url_pdf":"https://arxiv.org/pdf/2204.09433v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pp-matting-high-accuracy-natural-image","repo_url":"https://github.com/PaddlePaddle/PaddleSeg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"paddle","reach":null}],"tasks":[{"task_slug":"image-matting","task_name":"Image Matting"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-matting-on-composition-1k-1","task":"Image Matting","dataset":"Composition-1K","model":"PP-Matting","rank_in_archive_order":7,"of":13,"metrics":{"Conn":"45.4","Grad":"22.69","MSE":"5.0","SAD":"46.22"},"uses_additional_data":false},{"leaderboard":"/sota/image-matting-on-distinctions-646","task":"Image Matting","dataset":"Distinctions-646","model":"PP-Matting","rank_in_archive_order":4,"of":4,"metrics":{"Conn":"40.56","Grad":"43.91","MSE":"0.009","SAD":"40.69","Trimap":"×"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2204.09433","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}