{"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/joint-bilateral-learning-for-real-time","title":"Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer","arxiv_id":"2004.10955","date":"2020-04-23","proceeding":"ECCV 2020 8","authors":["Xide Xia","Meng Zhang","Tianfan Xue","Zheng Sun","Hui Fang","Brian Kulis","Jiawen Chen"],"abstract":"Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera. Recent approaches, based on deep neural networks, produce impressive results but are either too slow to run at practical resolutions, or still contain objectionable artifacts. We propose a new end-to-end model for photorealistic style transfer that is both fast and inherently generates photorealistic results. The core of our approach is a feed-forward neural network that learns local edge-aware affine transforms that automatically obey the photorealism constraint. When trained on a diverse set of images and a variety of styles, our model can robustly apply style transfer to an arbitrary pair of input images. Compared to the state of the art, our method produces visually superior results and is three orders of magnitude faster, enabling real-time performance at 4K on a mobile phone. We validate our method with ablation and user studies.","url_abs":"https://arxiv.org/abs/2004.10955v2","url_pdf":"https://arxiv.org/pdf/2004.10955v2.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":"joint-bilateral-learning-for-real-time","repo_url":"https://github.com/SystemErrorWang/Joint-Bilateral-Photorealistic-Style-Transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"joint-bilateral-learning-for-real-time","repo_url":"https://github.com/mousecpn/Joint-Bilateral-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"joint-bilateral-learning-for-real-time","repo_url":"https://github.com/xidexia/realtime_PST","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"4k","task_name":"4k"},{"task_slug":"style-transfer","task_name":"Style Transfer"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.10955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.10955"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mousecpn/Joint-Bilateral-Learning","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/SystemErrorWang/Joint-Bilateral-Photorealistic-Style-Transfer","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xidexia/realtime_PST","reach":{"status":"ok"}}],"summary":{"ran_draft_wrong":1,"ran_fixture":1,"unverified":1},"by_repo_kind":{"listed":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"16679a24b9881031","entry":"adaptive_instance_normalization","repo":"mousecpn/Joint-Bilateral-Learning","repo_kind":"listed","path":"model.py","file_url":"https://github.com/mousecpn/Joint-Bilateral-Learning/blob/HEAD/model.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"16679a24b9881031"}},{"code_sha256_prefix":"38eaf8511dc7bc9c","entry":"calc_mean_std","repo":"mousecpn/Joint-Bilateral-Learning","repo_kind":"listed","path":"model.py","file_url":"https://github.com/mousecpn/Joint-Bilateral-Learning/blob/HEAD/model.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"38eaf8511dc7bc9c"}},{"code_sha256_prefix":"cbb96c1d0826496d","entry":"resize","repo":"mousecpn/Joint-Bilateral-Learning","repo_kind":"listed","path":"model.py","file_url":"https://github.com/mousecpn/Joint-Bilateral-Learning/blob/HEAD/model.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cbb96c1d0826496d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}