# Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation

arXiv: https://arxiv.org/abs/2008.07132 · HTML: https://mlsanity.com/paper/2008.07132/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 19 of 19)

- [TADA! Text to Animatable Digital Avatars](https://mlsanity.com/paper/2308.10899/) (2023)
- [FT2TF: First-Person Statement Text-To-Talking Face Generation](https://mlsanity.com/paper/2312.05430/) (2023)
- [MeInGame: Create a Game Character Face from a Single Portrait](https://mlsanity.com/paper/2102.02371/) (2021)
- [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)
- [ASM: Adaptive Skinning Model for High-Quality 3D Face Modeling](https://mlsanity.com/paper/2304.09423/) (2023)
- [Bring Your Own Character: A Holistic Solution for Automatic Facial Animation Generation of Customized Characters](https://mlsanity.com/paper/2402.13724/) (2024)
- [StrandHead: Text to Hair-Disentangled 3D Head Avatars Using Human-Centric Priors](https://mlsanity.com/paper/2412.11586/) (2024)
- [SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines](https://mlsanity.com/paper/2301.08153/) (2023)
- [Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers](https://mlsanity.com/paper/2205.06305/) (2022)
- [Tag-based annotation creates better avatars](https://mlsanity.com/paper/2302.07354/) (2023)
- [Snapmoji: Instant Generation of Animatable Dual-Stylized Avatars](https://mlsanity.com/paper/2503.11978/) (2025)
- [Sphere Face Model:A 3D Morphable Model with Hypersphere Manifold Latent Space](https://mlsanity.com/paper/2112.02238/) (2021)
- [ParGAN: Learning Real Parametrizable Transformations](https://mlsanity.com/paper/2211.04996/) (2022)
- [Democratizing the Creation of Animatable Facial Avatars](https://mlsanity.com/paper/2401.16534/) (2024)
- [EasyCraft: A Robust and Efficient Framework for Automatic Avatar Crafting](https://mlsanity.com/paper/2503.01158/) (2025)
- [Domain Engineering for Applied Monocular Reconstruction of Parametric Faces](https://mlsanity.com/paper/2209.02600/) (2022)
- ["Benefit Game: Alien Seaweed Swarms" -- Real-time Gamification of Digital Seaweed Ecology](https://mlsanity.com/paper/2408.17186/) (2024)
- [Unsupervised Cross-Domain Regression for Fine-grained 3D Game Character Reconstruction](https://mlsanity.com/paper/2412.10430/) (2024)

## Abstract (arXiv)

With the rapid development of Role-Playing Games (RPGs), players are now allowed to edit the facial appearance of their in-game characters with their preferences rather than using default templates. This paper proposes a game character auto-creation framework that generates in-game characters according to a player's input face photo. Different from the previous methods that are designed based on neural style transfer or monocular 3D face reconstruction, we re-formulate the character auto-creation process in a different point of view: by predicting a large set of physically meaningful facial parameters under a self-supervised learning paradigm. Instead of updating facial parameters iteratively at the input end of the renderer as suggested by previous methods, which are time-consuming, we introduce a facial parameter translator so that the creation can be done efficiently through a single forward propagation from the face embeddings to parameters, with a considerable 1000x computational speedup. Despite its high efficiency, the interactivity is preserved in our method where users are allowed to optionally fine-tune the facial parameters on our creation according to their needs. Our approach also shows better robustness than previous methods, especially for those photos with head-pose variance. Comparison results and ablation analysis on seven public face verification datasets suggest the effectiveness of our method.