{"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/synthesizing-normalized-faces-from-facial","title":"Synthesizing Normalized Faces from Facial Identity Features","arxiv_id":"1701.04851","date":"2017-01-17","proceeding":"CVPR 2017 7","authors":["Forrester Cole","David Belanger","Dilip Krishnan","Aaron Sarna","Inbar Mosseri","William T. Freeman"],"abstract":"We present a method for synthesizing a frontal, neutral-expression image of a\nperson's face given an input face photograph. This is achieved by learning to\ngenerate facial landmarks and textures from features extracted from a\nfacial-recognition network. Unlike previous approaches, our encoding feature\nvector is largely invariant to lighting, pose, and facial expression.\nExploiting this invariance, we train our decoder network using only frontal,\nneutral-expression photographs. Since these photographs are well aligned, we\ncan decompose them into a sparse set of landmark points and aligned texture\nmaps. The decoder then predicts landmarks and textures independently and\ncombines them using a differentiable image warping operation. The resulting\nimages can be used for a number of applications, such as analyzing facial\nattributes, exposure and white balance adjustment, or creating a 3-D avatar.","url_abs":"http://arxiv.org/abs/1701.04851v4","url_pdf":"http://arxiv.org/pdf/1701.04851v4.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":"synthesizing-normalized-faces-from-facial","repo_url":"https://github.com/nabeel3133/3D-texture-fitting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.04851","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.04851"}},"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/nabeel3133/3D-texture-fitting","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":0,"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":"06024fc4fb06c8e5","entry":"str2bool","repo":"nabeel3133/3D-texture-fitting","repo_kind":"listed","path":"utils/inference.py","file_url":"https://github.com/nabeel3133/3D-texture-fitting/blob/HEAD/utils/inference.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":"06024fc4fb06c8e5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}