# Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1909.02215 · HTML: https://mlsanity.com/paper/1909.02215/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 30 of 34)

- [ClipFace: Text-guided Editing of Textured 3D Morphable Models](https://mlsanity.com/paper/2212.01406/) (2022)
- [AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs](https://mlsanity.com/paper/2112.05957/) (2021)
- [FitMe: Deep Photorealistic 3D Morphable Model Avatars](https://mlsanity.com/paper/2305.09641/) (2023)
- [Text and Image Guided 3D Avatar Generation and Manipulation](https://mlsanity.com/paper/2202.06079/) (2022)
- [Relightify: Relightable 3D Faces from a Single Image via Diffusion Models](https://mlsanity.com/paper/2305.06077/) (2023)
- [Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction](https://mlsanity.com/paper/2105.07474/) (2021)
- [High-Fidelity 3D Face Generation from Natural Language Descriptions](https://mlsanity.com/paper/2305.03302/) (2023)
- [Facial Geometric Detail Recovery via Implicit Representation](https://mlsanity.com/paper/2203.09692/) (2022)
- [Towards Realistic Generative 3D Face Models](https://mlsanity.com/paper/2304.12483/) (2023)
- [Inverting Generative Adversarial Renderer for Face Reconstruction](https://mlsanity.com/paper/2105.02431/) (2021)
- [Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance](https://mlsanity.com/paper/2501.05379/) (2025)
- [3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces](https://mlsanity.com/paper/2111.12448/) (2021)
- [UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures](https://mlsanity.com/paper/2401.11078/) (2024)
- [Simulated Adversarial Testing of Face Recognition Models](https://mlsanity.com/paper/2106.04569/) (2021)
- [3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling](https://mlsanity.com/paper/2209.07366/) (2022)
- [PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation](https://mlsanity.com/paper/2303.07337/) (2023)
- [Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance](https://mlsanity.com/paper/2208.14263/) (2022)
- [Fast Text-to-3D-Aware Face Generation and Manipulation via Direct Cross-modal Mapping and Geometric Regularization](https://mlsanity.com/paper/2403.06702/) (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)
- [3D Generative Model Latent Disentanglement via Local Eigenprojection](https://mlsanity.com/paper/2302.12798/) (2023)
- [FitDiff: Robust monocular 3D facial shape and reflectance estimation using Diffusion Models](https://mlsanity.com/paper/2312.04465/) (2023)
- [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)
- [Physically-Based Face Rendering for NIR-VIS Face Recognition](https://mlsanity.com/paper/2211.06408/) (2022)
- [Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map](https://mlsanity.com/paper/2308.13245/) (2023)
- [3D-aware Image Generation and Editing with Multi-modal Conditions](https://mlsanity.com/paper/2403.06470/) (2024)
- [REFA: Real-time Egocentric Facial Animations for Virtual Reality](https://mlsanity.com/paper/2601.03507/) (2026)
- [Efficient Hair Style Transfer with Generative Adversarial Networks](https://mlsanity.com/paper/2210.12524/) (2022)
- [3D Face Arbitrary Style Transfer](https://mlsanity.com/paper/2303.07709/) (2023)

## Abstract (arXiv)

Generating realistic 3D faces is of high importance for computer graphics and computer vision applications. Generally, research on 3D face generation revolves around linear statistical models of the facial surface. Nevertheless, these models cannot represent faithfully either the facial texture or the normals of the face, which are very crucial for photo-realistic face synthesis. Recently, it was demonstrated that Generative Adversarial Networks (GANs) can be used for generating high-quality textures of faces. Nevertheless, the generation process either omits the geometry and normals, or independent processes are used to produce 3D shape information. In this paper, we present the first methodology that generates high-quality texture, shape, and normals jointly, which can be used for photo-realistic synthesis. To do so, we propose a novel GAN that can generate data from different modalities while exploiting their correlations. Furthermore, we demonstrate how we can condition the generation on the expression and create faces with various facial expressions. The qualitative results shown in this paper are compressed due to size limitations, full-resolution results and the accompanying video can be found in the supplementary documents. The code and models are available at the project page: https://github.com/barisgecer/TBGAN.