# FitMe: Deep Photorealistic 3D Morphable Model Avatars

arXiv: https://arxiv.org/abs/2305.09641 · HTML: https://mlsanity.com/paper/2305.09641/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 78 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 30 of 45)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [GANSpace: Discovering Interpretable GAN Controls](https://mlsanity.com/paper/2004.02546/) (2020)
- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)
- [Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning](https://mlsanity.com/paper/1904.01786/) (2019)
- [StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows](https://mlsanity.com/paper/2008.02401/) (2020)
- [3D Morphable Face Models -- Past, Present and Future](https://mlsanity.com/paper/1909.01815/) (2019)
- [FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction](https://mlsanity.com/paper/2003.13989/) (2020)
- [Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision](https://mlsanity.com/paper/1905.06817/) (2019)
- [Nonlinear 3D Face Morphable Model](https://mlsanity.com/paper/1804.03786/) (2018)
- [GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction](https://mlsanity.com/paper/1902.05978/) (2019)
- [SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wild](https://mlsanity.com/paper/1712.01261/) (2017)
- [Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network](https://mlsanity.com/paper/1612.04904/) (2016)
- [Unsupervised Training for 3D Morphable Model Regression](https://mlsanity.com/paper/1806.06098/) (2018)
- [Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research](https://mlsanity.com/paper/1911.05063/) (2019)
- [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)
- [Neural Face Editing with Intrinsic Image Disentangling](https://mlsanity.com/paper/1704.04131/) (2017)
- [Towards a complete 3D morphable model of the human head](https://mlsanity.com/paper/1911.08008/) (2019)
- [A Morphable Face Albedo Model](https://mlsanity.com/paper/2004.02711/) (2020)
- [Combining 3D Morphable Models: A Large scale Face-and-Head Model](https://mlsanity.com/paper/1903.03785/) (2019)
- [AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs](https://mlsanity.com/paper/2112.05957/) (2021)

## Built on by (most cited first, 30 of 38)

- [SMIRK: 3D Facial Expressions through Analysis-by-Neural-Synthesis](https://mlsanity.com/paper/2404.04104/) (2024)
- [MonoNPHM: Dynamic Head Reconstruction from Monocular Videos](https://mlsanity.com/paper/2312.06740/) (2023)
- [VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment](https://mlsanity.com/paper/2312.04651/) (2023)
- [MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading](https://mlsanity.com/paper/2312.13091/) (2023)
- [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)
- [UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures](https://mlsanity.com/paper/2401.11078/) (2024)
- [Synthetic Face Datasets Generation via Latent Space Exploration from Brownian Identity Diffusion](https://mlsanity.com/paper/2405.00228/) (2024)
- [LVFace: Progressive Cluster Optimization for Large Vision Models in Face Recognition](https://mlsanity.com/paper/2501.13420/) (2025)
- [High-Quality Facial Geometry and Appearance Capture at Home](https://mlsanity.com/paper/2312.03442/) (2023)
- [Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture](https://mlsanity.com/paper/2406.00440/) (2024)
- [Make-It-Vivid: Dressing Your Animatable Biped Cartoon Characters from Text](https://mlsanity.com/paper/2403.16897/) (2024)
- [Makeup Prior Models for 3D Facial Makeup Estimation and Applications](https://mlsanity.com/paper/2403.17761/) (2024)
- [FreeUV: Ground-Truth-Free Realistic Facial UV Texture Recovery via Cross-Assembly Inference Strategy](https://mlsanity.com/paper/2503.17197/) (2025)
- [FastAvatar: Instant 3D Gaussian Splatting for Faces from Single Unconstrained Poses](https://mlsanity.com/paper/2508.18389/) (2025)
- [BodyShapeGPT: SMPL Body Shape Manipulation with LLMs](https://mlsanity.com/paper/2410.03556/) (2024)
- [FitDiff: Robust monocular 3D facial shape and reflectance estimation using Diffusion Models](https://mlsanity.com/paper/2312.04465/) (2023)
- [Locally Adaptive Neural 3D Morphable Models](https://mlsanity.com/paper/2401.02937/) (2024)
- [Monocular Identity-Conditioned Facial Reflectance Reconstruction](https://mlsanity.com/paper/2404.00301/) (2024)
- [ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling](https://mlsanity.com/paper/2405.16570/) (2024)
- [S2TD-Face: Reconstruct a Detailed 3D Face with Controllable Texture from a Single Sketch](https://mlsanity.com/paper/2408.01218/) (2024)
- [SFDM: Robust Decomposition of Geometry and Reflectance for Realistic Face Rendering from Sparse-view Images](https://mlsanity.com/paper/2312.06085/) (2023)
- [TeGA: Texture Space Gaussian Avatars for High-Resolution Dynamic Head Modeling](https://mlsanity.com/paper/2505.05672/) (2025)
- [SpinMeRound: Consistent Multi-View Identity Generation Using Diffusion Models](https://mlsanity.com/paper/2504.10716/) (2025)
- [High-Fidelity Facial Albedo Estimation via Texture Quantization](https://mlsanity.com/paper/2406.13149/) (2024)
- [Learning to Decouple the Lights for 3D Face Texture Modeling](https://mlsanity.com/paper/2412.08524/) (2024)
- [AvatarTex: High-Fidelity Facial Texture Reconstruction from Single-Image Stylized Avatars](https://mlsanity.com/paper/2511.06721/) (2025)
- [WildCap: Facial Albedo Capture in the Wild via Hybrid Inverse Rendering](https://mlsanity.com/paper/2512.11237/) (2025)
- [FiCA: Feed-forward instant Gaussian Codec Avatars from a Single Portrait Image](https://mlsanity.com/paper/2606.24232/) (2026)
- [Monocular Avatar Reconstruction via Cascaded Diffusion Priors and UV-Space Differentiable Shading](https://mlsanity.com/paper/2606.28144/) (2026)

## Abstract (arXiv)

In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering optimization pipeline, that can be used to acquire high-fidelity renderable human avatars from single or multiple images. The model consists of a multi-modal style-based generator, that captures facial appearance in terms of diffuse and specular reflectance, and a PCA-based shape model. We employ a fast differentiable rendering process that can be used in an optimization pipeline, while also achieving photorealistic facial shading. Our optimization process accurately captures both the facial reflectance and shape in high-detail, by exploiting the expressivity of the style-based latent representation and of our shape model. FitMe achieves state-of-the-art reflectance acquisition and identity preservation on single "in-the-wild" facial images, while it produces impressive scan-like results, when given multiple unconstrained facial images pertaining to the same identity. In contrast with recent implicit avatar reconstructions, FitMe requires only one minute and produces relightable mesh and texture-based avatars, that can be used by end-user applications.