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SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery Chatziagapi, Aggelina Athar, ShahRukh Moreno-Noguer, Francesc Samaras, Dimitris Institut de Robòtica i Informàtica Industrial Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI Àrees temàtiques de la UPC::Informàtica::Automàtica i control Three-dimensional imaging Geometry Hair Solid modeling Three-dimensional displays Shape Optimization methods Lighting Imatgeria tridimensional © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. We present SIDER (Single-Image neural optimization for facial geometric DEtail Recovery), a novel photometric optimization method that recovers detailed facial geometry from a single image in an unsupervised manner. Inspired by classical techniques of coarse-to-fine optimization and recent advances in implicit neural representations of 3D shape, SIDER combines a geometry prior based on statistical models and Signed Distance Functions (SDFs) to recover facial details from single images. First, it estimates a coarse geometry using a morphable model represented as an SDF. Next, it reconstructs facial geometry details by optimizing a photometric loss with respect to the ground truth image. In contrast to prior work, SIDER does not rely on any dataset priors and does not require additional supervision from multiple views, lighting changes or ground truth 3D shape. Extensive qualitative and quantitative evaluation demonstrates that our method achieves state-of-the-art on facial geometric detail recovery, using only a single in the-wild image. Peer Reviewed Postprint (author's final draft) 2021 Conference report Chatziagapi, A. [et al.]. SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery. A: International Conference on 3D Vision. "2021 International Conference on 3D Vision: 3DV 2021: virtual conference 1-3 December 2021: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 815-824. ISBN 978-1-6654-2688-6. DOI 10.1109/3DV53792.2021.00090. 978-1-6654-2688-6 https://hdl.handle.net/2117/366344 10.1109/3DV53792.2021.00090 eng https://ieeexplore.ieee.org/document/9665937 Open Access 10 p. application/pdf Institute of Electrical and Electronics Engineers (IEEE)