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Our technique includes foreground estimation via alpha matting, relighting, and compositing. We demonstrate that each of these stages can be tackled in a sequential pipeline without the use of priors (e.g. known background or known illumination) and with no specialized acquisition techniques, using only a single RGB portrait image and a novel, target HDR lighting environment as inputs. We train our model using relit portraits of subjects captured in a light stage computational illumination system, which records multiple lighting conditions, high quality geometry, and accurate alpha mattes. To perform realistic relighting for compositing, we introduce a novel per-pixel lighting representation in a deep learning framework, which explicitly models the diffuse and the specular components of appearance, producing relit portraits with convincingly rendered non-Lambertian effects like specular highlights. Multiple experiments and comparisons show the effectiveness of the proposed approach when applied to in-the-wild images.<\/jats:p>","DOI":"10.1145\/3450626.3459872","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:04:26Z","timestamp":1626739466000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":139,"title":["Total relighting"],"prefix":"10.1145","volume":"40","author":[{"given":"Rohit","family":"Pandey","sequence":"first","affiliation":[{"name":"Google Research"}]},{"given":"Sergio Orts","family":"Escolano","sequence":"additional","affiliation":[{"name":"Google Research"}]},{"given":"Chloe","family":"Legendre","sequence":"additional","affiliation":[{"name":"Google Research"}]},{"given":"Christian","family":"H\u00e4ne","sequence":"additional","affiliation":[{"name":"Google Research"}]},{"given":"Sofien","family":"Bouaziz","sequence":"additional","affiliation":[{"name":"Google Research"}]},{"given":"Christoph","family":"Rhemann","sequence":"additional","affiliation":[{"name":"Google Research"}]},{"given":"Paul","family":"Debevec","sequence":"additional","affiliation":[{"name":"Google Research"}]},{"given":"Sean","family":"Fanello","sequence":"additional","affiliation":[{"name":"Google Research"}]}],"member":"320","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2377712"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368850.3383439"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/311535.311553"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00891"},{"key":"e_1_2_2_5_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Chen Guanying","unstructured":"Guanying Chen , Kai Han , and Kwan-Yee K. 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