{"date_updated":"2023-08-11T11:08:35Z","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","_id":"9957","status":"public","title":"Monocular reconstruction of neural face reflectance fields","external_id":{"arxiv":["2008.10247"],"isi":["000739917304096"]},"citation":{"chicago":"B R, Mallikarjun, Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, Bernd Bickel, Hans-Peter Seidel, Hanspeter Pfister, Wojciech Matusik, Mohamed Elgharib, and Christian Theobalt. “Monocular Reconstruction of Neural Face Reflectance Fields.” In <i>Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</i>, 4791–4800. IEEE, 2021. <a href=\"https://doi.org/10.1109/CVPR46437.2021.00476\">https://doi.org/10.1109/CVPR46437.2021.00476</a>.","apa":"B R, M., Tewari, A., Oh, T.-H., Weyrich, T., Bickel, B., Seidel, H.-P., … Theobalt, C. (2021). Monocular reconstruction of neural face reflectance fields. In <i>Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</i> (pp. 4791–4800). Nashville, TN, United States; Virtual: IEEE. <a href=\"https://doi.org/10.1109/CVPR46437.2021.00476\">https://doi.org/10.1109/CVPR46437.2021.00476</a>","mla":"B R, Mallikarjun, et al. “Monocular Reconstruction of Neural Face Reflectance Fields.” <i>Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</i>, IEEE, 2021, pp. 4791–800, doi:<a href=\"https://doi.org/10.1109/CVPR46437.2021.00476\">10.1109/CVPR46437.2021.00476</a>.","ieee":"M. B R <i>et al.</i>, “Monocular reconstruction of neural face reflectance fields,” in <i>Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</i>, Nashville, TN, United States; Virtual, 2021, pp. 4791–4800.","short":"M. B R, A. Tewari, T.-H. Oh, T. Weyrich, B. Bickel, H.-P. Seidel, H. Pfister, W. Matusik, M. Elgharib, C. Theobalt, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2021, pp. 4791–4800.","ista":"B R M, Tewari A, Oh T-H, Weyrich T, Bickel B, Seidel H-P, Pfister H, Matusik W, Elgharib M, Theobalt C. 2021. Monocular reconstruction of neural face reflectance fields. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 4791–4800.","ama":"B R M, Tewari A, Oh T-H, et al. Monocular reconstruction of neural face reflectance fields. In: <i>Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</i>. IEEE; 2021:4791-4800. doi:<a href=\"https://doi.org/10.1109/CVPR46437.2021.00476\">10.1109/CVPR46437.2021.00476</a>"},"oa_version":"Preprint","ddc":["000"],"type":"conference","file_date_updated":"2021-08-24T06:02:15Z","year":"2021","date_published":"2021-09-01T00:00:00Z","day":"01","publication_status":"published","page":"4791-4800","language":[{"iso":"eng"}],"conference":{"location":"Nashville, TN, United States; Virtual","name":"CVPR: Conference on Computer Vision and Pattern Recognition","end_date":"2021-06-25","start_date":"2021-06-20"},"doi":"10.1109/CVPR46437.2021.00476","date_created":"2021-08-24T06:03:00Z","file":[{"file_id":"9958","relation":"main_file","file_size":4746649,"date_created":"2021-08-24T06:02:15Z","content_type":"application/pdf","creator":"bbickel","file_name":"R_Monocular_Reconstruction_of_Neural_Face_Reflectance_Fields_CVPR_2021_paper[1].pdf","access_level":"open_access","checksum":"961db0bde76dd87cf833930080bb9f38","date_updated":"2021-08-24T06:02:15Z"}],"publication":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","scopus_import":"1","quality_controlled":"1","isi":1,"author":[{"last_name":"B R","first_name":"Mallikarjun","full_name":"B R, Mallikarjun"},{"last_name":"Tewari","first_name":"Ayush","full_name":"Tewari, Ayush"},{"full_name":"Oh, Tae-Hyun","first_name":"Tae-Hyun","last_name":"Oh"},{"full_name":"Weyrich, Tim","first_name":"Tim","last_name":"Weyrich"},{"full_name":"Bickel, Bernd","first_name":"Bernd","orcid":"0000-0001-6511-9385","id":"49876194-F248-11E8-B48F-1D18A9856A87","last_name":"Bickel"},{"full_name":"Seidel, Hans-Peter","last_name":"Seidel","first_name":"Hans-Peter"},{"full_name":"Pfister, Hanspeter","first_name":"Hanspeter","last_name":"Pfister"},{"first_name":"Wojciech","last_name":"Matusik","full_name":"Matusik, Wojciech"},{"full_name":"Elgharib, Mohamed","first_name":"Mohamed","last_name":"Elgharib"},{"full_name":"Theobalt, Christian","last_name":"Theobalt","first_name":"Christian"}],"publisher":"IEEE","publication_identifier":{"isbn":["978-166544509-2"],"issn":["1063-6919"]},"department":[{"_id":"BeBi"}],"month":"09","acknowledgement":"We thank Tarun Yenamandra and Duarte David for helping us with the comparisons. This work was supported by the\r\nERC Consolidator Grant 4DReply (770784). We also acknowledge support from InterDigital.","article_processing_charge":"No","oa":1,"has_accepted_license":"1","abstract":[{"lang":"eng","text":"The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing. Most existing methods for estimating the face reflectance from a monocular image assume faces to be diffuse with very few approaches adding a specular component. This still leaves out important perceptual aspects of reflectance as higher-order global illumination effects and self-shadowing are not modeled. We present a new neural representation for face reflectance where we can estimate all components of the reflectance responsible for the final appearance from a single monocular image. Instead of modeling each component of the reflectance separately using parametric models, our neural representation allows us to generate a basis set of faces in a geometric deformation-invariant space, parameterized by the input light direction, viewpoint and face geometry. We learn to reconstruct this reflectance field of a face just from a monocular image, which can be used to render the face from any viewpoint in any light condition. Our method is trained on a light-stage training dataset, which captures 300 people illuminated with 150 light conditions from 8 viewpoints. We show that our method outperforms existing monocular reflectance reconstruction methods, in terms of photorealism due to better capturing of physical premitives, such as sub-surface scattering, specularities, self-shadows and other higher-order effects."}]}