# MeInGame: Create a Game Character Face from a Single Portrait

arXiv: https://arxiv.org/abs/2102.02371 · HTML: https://mlsanity.com/paper/2102.02371/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 4 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning](https://mlsanity.com/paper/1904.01786/) (2019)
- [Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer](https://mlsanity.com/paper/1908.01210/) (2019)
- [FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction](https://mlsanity.com/paper/2003.13989/) (2020)
- [GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction](https://mlsanity.com/paper/1902.05978/) (2019)
- [Morphable Face Models - An Open Framework](https://mlsanity.com/paper/1709.08398/) (2017)
- [Unsupervised Training for 3D Morphable Model Regression](https://mlsanity.com/paper/1806.06098/) (2018)
- [Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz](https://mlsanity.com/paper/1712.02859/) (2017)
- [UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition](https://mlsanity.com/paper/1712.04695/) (2017)
- [Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks](https://mlsanity.com/paper/2003.05653/) (2020)
- [Dense 3D Face Decoding over 2500FPS: Joint Texture & Shape Convolutional Mesh Decoders](https://mlsanity.com/paper/1904.03525/) (2019)
- [Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation](https://mlsanity.com/paper/2008.07132/) (2020)
- [The Do's and Don'ts for CNN-based Face Verification](https://mlsanity.com/paper/1705.07426/) (2017)

## Built on by (most cited first, 16 of 16)

- [REALY: Rethinking the Evaluation of 3D Face Reconstruction](https://mlsanity.com/paper/2203.09729/) (2022)
- [Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation](https://mlsanity.com/paper/2303.01311/) (2023)
- [AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging](https://mlsanity.com/paper/2211.07818/) (2022)
- [Meta-Learning Empowered Meta-Face: Personalized Speaking Style Adaptation for Audio-Driven 3D Talking Face Animation](https://mlsanity.com/paper/2408.09357/) (2024)
- [VGG-Tex: A Vivid Geometry-Guided Facial Texture Estimation Model for High Fidelity Monocular 3D Face Reconstruction](https://mlsanity.com/paper/2409.09740/) (2024)
- [High-Quality 3D Face Reconstruction with Affine Convolutional Networks](https://mlsanity.com/paper/2310.14237/) (2023)
- [FaceRefiner: High-Fidelity Facial Texture Refinement with Differentiable Rendering-based Style Transfer](https://mlsanity.com/paper/2601.04520/) (2026)
- [Tag-based annotation creates better avatars](https://mlsanity.com/paper/2302.07354/) (2023)
- [Sphere Face Model:A 3D Morphable Model with Hypersphere Manifold Latent Space](https://mlsanity.com/paper/2112.02238/) (2021)
- [Applied monocular reconstruction of parametric faces with domain engineering](https://mlsanity.com/paper/2208.02935/) (2022)
- [Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art](https://mlsanity.com/paper/2311.10129/) (2023)
- [Democratizing the Creation of Animatable Facial Avatars](https://mlsanity.com/paper/2401.16534/) (2024)
- [Domain Engineering for Applied Monocular Reconstruction of Parametric Faces](https://mlsanity.com/paper/2209.02600/) (2022)
- [Controllable Face Manipulation and UV Map Generation by Self-supervised Learning](https://mlsanity.com/paper/2209.12050/) (2022)
- [MUNCH: Modelling Unique 'N Controllable Heads](https://mlsanity.com/paper/2310.02753/) (2023)
- [Heuristics for AI-driven Graphical Asset Generation Tools in Game Design and Development Pipelines: A User-Centred Approach](https://mlsanity.com/paper/2503.02703/) (2025)

## Abstract (arXiv)

Many deep learning based 3D face reconstruction methods have been proposed recently, however, few of them have applications in games. Current game character customization systems either require players to manually adjust considerable face attributes to obtain the desired face, or have limited freedom of facial shape and texture. In this paper, we propose an automatic character face creation method that predicts both facial shape and texture from a single portrait, and it can be integrated into most existing 3D games. Although 3D Morphable Face Model (3DMM) based methods can restore accurate 3D faces from single images, the topology of 3DMM mesh is different from the meshes used in most games. To acquire fidelity texture, existing methods require a large amount of face texture data for training, while building such datasets is time-consuming and laborious. Besides, such a dataset collected under laboratory conditions may not generalized well to in-the-wild situations. To tackle these problems, we propose 1) a low-cost facial texture acquisition method, 2) a shape transfer algorithm that can transform the shape of a 3DMM mesh to games, and 3) a new pipeline for training 3D game face reconstruction networks. The proposed method not only can produce detailed and vivid game characters similar to the input portrait, but can also eliminate the influence of lighting and occlusions. Experiments show that our method outperforms state-of-the-art methods used in games.