# Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation

arXiv: https://arxiv.org/abs/2303.01311 · HTML: https://mlsanity.com/paper/2303.01311/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 53 references that are in the map.
- First posted on arXiv 2 Mar 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Zero-Shot Text-Guided Object Generation with Dream Fields](https://mlsanity.com/paper/2112.01455/) (2021)
- [CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields](https://mlsanity.com/paper/2112.05139/) (2021)
- [Snapshot Ensembles: Train 1, get M for free](https://mlsanity.com/paper/1704.00109/) (2017)
- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)
- [CLIP-Mesh: Generating textured meshes from text using pretrained image-text models](https://mlsanity.com/paper/2203.13333/) (2022)
- [CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation](https://mlsanity.com/paper/2110.02624/) (2021)
- [StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators](https://mlsanity.com/paper/2108.00946/) (2021)
- [MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction](https://mlsanity.com/paper/1703.10580/) (2017)
- [A Survey on Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2008.10937/) (2020)
- [Morphable Face Models - An Open Framework](https://mlsanity.com/paper/1709.08398/) (2017)
- [Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network](https://mlsanity.com/paper/1612.04904/) (2016)
- [Talk-to-Edit: Fine-Grained Facial Editing via Dialog](https://mlsanity.com/paper/2109.04425/) (2021)
- [HairCLIP: Design Your Hair by Text and Reference Image](https://mlsanity.com/paper/2112.05142/) (2021)
- [Learning Detailed Face Reconstruction from a Single Image](https://mlsanity.com/paper/1611.05053/) (2016)
- [Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression](https://mlsanity.com/paper/1703.07834/) (2017)
- [AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars](https://mlsanity.com/paper/2205.08535/) (2022)

## Built on by (most cited first, 15 of 15)

- [TADA! Text to Animatable Digital Avatars](https://mlsanity.com/paper/2308.10899/) (2023)
- [HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting](https://mlsanity.com/paper/2311.17061/) (2023)
- [Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era](https://mlsanity.com/paper/2305.06131/) (2023)
- [HeadSculpt: Crafting 3D Head Avatars with Text](https://mlsanity.com/paper/2306.03038/) (2023)
- [Text-Guided Generation and Editing of Compositional 3D Avatars](https://mlsanity.com/paper/2309.07125/) (2023)
- [Simulating the Real World: A Unified Survey of Multimodal Generative Models](https://mlsanity.com/paper/2503.04641/) (2025)
- [GS-VTON: Controllable 3D Virtual Try-on with Gaussian Splatting](https://mlsanity.com/paper/2410.05259/) (2024)
- [EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models](https://mlsanity.com/paper/2410.07133/) (2024)
- [Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Models](https://mlsanity.com/paper/2307.04859/) (2023)
- [DoraCycle: Domain-Oriented Adaptation of Unified Generative Model in Multimodal Cycles](https://mlsanity.com/paper/2503.03651/) (2025)
- [TeRA: Rethinking Text-guided Realistic 3D Avatar Generation](https://mlsanity.com/paper/2509.02466/) (2025)
- [Minecraft-ify: Minecraft Style Image Generation with Text-guided Image Editing for In-Game Application](https://mlsanity.com/paper/2402.05448/) (2024)
- [EasyCraft: A Robust and Efficient Framework for Automatic Avatar Crafting](https://mlsanity.com/paper/2503.01158/) (2025)
- [P-Flow: Prompting Visual Effects Generation](https://mlsanity.com/paper/2603.22091/) (2026)
- [RAGMesh with FaME-G2E: Long-Form Text-Driven 3D Face Generation and Editing](https://mlsanity.com/paper/2608.09186/) (2026)

## Abstract (arXiv)

Recent popular Role-Playing Games (RPGs) saw the great success of character auto-creation systems. The bone-driven face model controlled by continuous parameters (like the position of bones) and discrete parameters (like the hairstyles) makes it possible for users to personalize and customize in-game characters. Previous in-game character auto-creation systems are mostly image-driven, where facial parameters are optimized so that the rendered character looks similar to the reference face photo. This paper proposes a novel text-to-parameter translation method (T2P) to achieve zero-shot text-driven game character auto-creation. With our method, users can create a vivid in-game character with arbitrary text description without using any reference photo or editing hundreds of parameters manually. In our method, taking the power of large-scale pre-trained multi-modal CLIP and neural rendering, T2P searches both continuous facial parameters and discrete facial parameters in a unified framework. Due to the discontinuous parameter representation, previous methods have difficulty in effectively learning discrete facial parameters. T2P, to our best knowledge, is the first method that can handle the optimization of both discrete and continuous parameters. Experimental results show that T2P can generate high-quality and vivid game characters with given text prompts. T2P outperforms other SOTA text-to-3D generation methods on both objective evaluations and subjective evaluations.