# MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading

arXiv: https://arxiv.org/abs/2312.13091 · HTML: https://mlsanity.com/paper/2312.13091/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 65 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 30 of 41)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [Learning an Animatable Detailed 3D Face Model from In-The-Wild Images](https://mlsanity.com/paper/2012.04012/) (2020)
- [Generating 3D faces using Convolutional Mesh Autoencoders](https://mlsanity.com/paper/1807.10267/) (2018)
- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision](https://mlsanity.com/paper/1905.06817/) (2019)
- [Towards Metrical Reconstruction of Human Faces](https://mlsanity.com/paper/2204.06607/) (2022)
- [MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction](https://mlsanity.com/paper/1703.10580/) (2017)
- [3D-CODED : 3D Correspondences by Deep Deformation](https://mlsanity.com/paper/1806.05228/) (2018)
- [Nonlinear 3D Face Morphable Model](https://mlsanity.com/paper/1804.03786/) (2018)
- [Face Alignment in Full Pose Range: A 3D Total Solution](https://mlsanity.com/paper/1804.01005/) (2018)
- [Differentiable Rendering: A Survey](https://mlsanity.com/paper/2006.12057/) (2020)
- [FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset](https://mlsanity.com/paper/2203.14057/) (2022)
- [Learning Formation of Physically-Based Face Attributes](https://mlsanity.com/paper/2004.03458/) (2020)
- [Towards High-fidelity Nonlinear 3D Face Morphable Model](https://mlsanity.com/paper/1904.04933/) (2019)
- [AvatarMe: Realistically Renderable 3D Facial Reconstruction "in-the-wild"](https://mlsanity.com/paper/2003.13845/) (2020)
- [FML: Face Model Learning from Videos](https://mlsanity.com/paper/1812.07603/) (2018)
- [Learning Detailed Face Reconstruction from a Single Image](https://mlsanity.com/paper/1611.05053/) (2016)
- [UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition](https://mlsanity.com/paper/1712.04695/) (2017)
- [AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs](https://mlsanity.com/paper/2112.05957/) (2021)
- [Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracing](https://mlsanity.com/paper/2103.15432/) (2021)
- [A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images](https://mlsanity.com/paper/2302.14434/) (2023)
- [Photo-Realistic Facial Details Synthesis from Single Image](https://mlsanity.com/paper/1903.10873/) (2019)

## Built on by (most cited first, 18 of 18)

- [HRAvatar: High-Quality and Relightable Gaussian Head Avatar](https://mlsanity.com/paper/2503.08224/) (2025)
- [Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance](https://mlsanity.com/paper/2501.05379/) (2025)
- [Synthetic Prior for Few-Shot Drivable Head Avatar Inversion](https://mlsanity.com/paper/2501.06903/) (2025)
- [FreeUV: Ground-Truth-Free Realistic Facial UV Texture Recovery via Cross-Assembly Inference Strategy](https://mlsanity.com/paper/2503.17197/) (2025)
- [Facial Appearance Capture at Home with Patch-Level Reflectance Prior](https://mlsanity.com/paper/2506.03478/) (2025)
- [SEREP: Semantic Facial Expression Representation for Robust In-the-Wild Capture and Retargeting](https://mlsanity.com/paper/2412.14371/) (2024)
- [Monocular Facial Appearance Capture in the Wild](https://mlsanity.com/paper/2412.12765/) (2024)
- [WildCap: Facial Albedo Capture in the Wild via Hybrid Inverse Rendering](https://mlsanity.com/paper/2512.11237/) (2025)
- [Skullptor: High Fidelity 3D Head Reconstruction in Seconds with Multi-View Normal Prediction](https://mlsanity.com/paper/2602.21100/) (2026)
- [High-Fidelity Single-Image Head Modeling with Industry-Grade Topology](https://mlsanity.com/paper/2605.04524/) (2026)
- [Monocular Avatar Reconstruction via Cascaded Diffusion Priors and UV-Space Differentiable Shading](https://mlsanity.com/paper/2606.28144/) (2026)
- [Geometry-Aware Texture Generation for 3D Head Modeling with Artist-driven Control](https://mlsanity.com/paper/2505.04387/) (2025)
- [Improving Facial Rig Semantics for Tracking and Retargeting](https://mlsanity.com/paper/2508.08429/) (2025)
- [Multi-scale Attention-Guided Intrinsic Decomposition and Rendering Pass Prediction for Facial Images](https://mlsanity.com/paper/2512.16511/) (2025)
- [Learning a Delighting Prior for Facial Appearance Capture in the Wild](https://mlsanity.com/paper/2605.05636/) (2026)
- [TOPOS: High-Fidelity and Efficient Industry-Grade 3D Head Generation](https://mlsanity.com/paper/2605.14594/) (2026)
- [UVFaceFusion: Fast Multi-view Topologically Consistent Face Reconstruction in the Wild via UV-space Neural Fusion](https://mlsanity.com/paper/2607.18798/) (2026)
- [FaceSnap: Real-Time Personalized Lightstage Facial Performance Capture](https://mlsanity.com/paper/2608.31033/) (2026)

## Abstract (arXiv)

Reconstructing an avatar from a portrait image has many applications in multimedia, but remains a challenging research problem. Extracting reflectance maps and geometry from one image is ill-posed: recovering geometry is a one-to-many mapping problem and reflectance and light are difficult to disentangle. Accurate geometry and reflectance can be captured under the controlled conditions of a light stage, but it is costly to acquire large datasets in this fashion. Moreover, training solely with this type of data leads to poor generalization with in-the-wild images. This motivates the introduction of MoSAR, a method for 3D avatar generation from monocular images. We propose a semi-supervised training scheme that improves generalization by learning from both light stage and in-the-wild datasets. This is achieved using a novel differentiable shading formulation. We show that our approach effectively disentangles the intrinsic face parameters, producing relightable avatars. As a result, MoSAR estimates a richer set of skin reflectance maps, and generates more realistic avatars than existing state-of-the-art methods. We also introduce a new dataset, named FFHQ-UV-Intrinsics, the first public dataset providing intrinsic face attributes at scale (diffuse, specular, ambient occlusion and translucency maps) for a total of 10k subjects. The project website and the dataset are available on the following link: https://ubisoft-laforge.github.io/character/mosar/