{"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/reenactgan-learning-to-reenact-faces-via","title":"ReenactGAN: Learning to Reenact Faces via Boundary Transfer","arxiv_id":"1807.11079","date":"2018-07-29","proceeding":"ECCV 2018 9","authors":["Wayne Wu","Yunxuan Zhang","Cheng Li","Chen Qian","Chen Change Loy"],"abstract":"We present a novel learning-based framework for face reenactment. The\nproposed method, known as ReenactGAN, is capable of transferring facial\nmovements and expressions from monocular video input of an arbitrary person to\na target person. Instead of performing a direct transfer in the pixel space,\nwhich could result in structural artifacts, we first map the source face onto a\nboundary latent space. A transformer is subsequently used to adapt the boundary\nof source face to the boundary of target face. Finally, a target-specific\ndecoder is used to generate the reenacted target face. Thanks to the effective\nand reliable boundary-based transfer, our method can perform photo-realistic\nface reenactment. In addition, ReenactGAN is appealing in that the whole\nreenactment process is purely feed-forward, and thus the reenactment process\ncan run in real-time (30 FPS on one GTX 1080 GPU). Dataset and model will be\npublicly available at\nhttps://wywu.github.io/projects/ReenactGAN/ReenactGAN.html","url_abs":"http://arxiv.org/abs/1807.11079v1","url_pdf":"http://arxiv.org/pdf/1807.11079v1.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":"reenactgan-learning-to-reenact-faces-via","repo_url":"https://github.com/wywu/ReenactGAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"face-reenactment","task_name":"Face Reenactment"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"talking-face-generation","task_name":"Talking Face Generation"},{"task_slug":"talking-head-generation","task_name":"Talking Head Generation"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1807.11079","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}