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Other methods are trained on high-quality face scans and do not generalize well to in-the-wild images. We present the first approach that regresses 3D face shape and animatable details that are specific to an individual but change with expression. Our model, DECA (Detailed Expression Capture and Animation), is trained to robustly produce a UV displacement map from a low-dimensional latent representation that consists of person-specific detail parameters and generic expression parameters, while a regressor is trained to predict detail, shape, albedo, expression, pose and illumination parameters from a single image. To enable this, we introduce a novel detail-consistency loss that disentangles person-specific details from expression-dependent wrinkles. This disentanglement allows us to synthesize realistic person-specific wrinkles by controlling expression parameters while keeping person-specific details unchanged. DECA is learned from in-the-wild images with no paired 3D supervision and achieves state-of-the-art shape reconstruction accuracy on two benchmarks. Qualitative results on in-the-wild data demonstrate DECA's robustness and its ability to disentangle identity- and expression-dependent details enabling animation of reconstructed faces. The model and code are publicly available at https:\/\/deca.is.tue.mpg.de.<\/jats:p>","DOI":"10.1145\/3450626.3459936","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:04:27Z","timestamp":1626739467000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":626,"title":["Learning an animatable detailed 3D face model from in-the-wild images"],"prefix":"10.1145","volume":"40","author":[{"given":"Yao","family":"Feng","sequence":"first","affiliation":[{"name":"Max Planck ETH Center for Learning System, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiwen","family":"Feng","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Intelligent Systems, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Black","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Intelligent Systems, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timo","family":"Bolkart","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Intelligent Systems, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4979--4989","author":"Abrevaya Victoria Fern\u00e1ndez","year":"2020","unstructured":"Victoria Fern\u00e1ndez Abrevaya , Adnane Boukhayma , Philip HS Torr , and Edmond Boyer . 2020 . 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