mlsanityData to 7 Oct 2026

SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery

arXiv 2108.05465 ↗First posted 11 Aug 2021cs.CVcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 11 Aug 2021.

Builds on

24 of 35 references.

References · 24

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  3. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  4. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  5. 2020Implicit Geometric Regularization for Learning Shapescited by 707
  6. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
  7. 2020Learning an Animatable Detailed 3D Face Model from In-The-Wild Imagescited by 475
  8. 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
  9. 2020Neural Unsigned Distance Fields for Implicit Function Learningcited by 265
  10. 2020Towards Fast, Accurate and Stable 3D Dense Face Alignmentcited by 219
  11. 2019Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervisioncited by 176
  12. 2018Nonlinear 3D Face Morphable Modelcited by 157
12 more
  1. 2021SMPLicit: Topology-aware Generative Model for Clothed Peoplecited by 154
  2. 2019Learning to Infer Implicit Surfaces without 3D Supervisioncited by 103
  3. 2018Unsupervised Training for 3D Morphable Model Regressioncited by 99
  4. 2017Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hzcited by 91
  5. 2019Towards High-fidelity Nonlinear 3D Face Morphable Modelcited by 91
  6. 2018FML: Face Model Learning from Videoscited by 78
  7. 2017Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regressioncited by 66
  8. 2017Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translationcited by 56
  9. 2017End-to-end 3D face reconstruction with deep neural networkscited by 47
  10. 20173D Face Morphable Models "In-the-Wild"cited by 44
  11. 20173D Face Reconstruction with Geometry Details from a Single Imagecited by 27
  12. 2020Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Studycited by 18

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Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 1.

9 papers.