Hunyuan-Game：工业级智能游戏创作模型

Hunyuan-Game: Industrial-grade Intelligent Game Creation Model

May 20, 2025

作者: Ruihuang Li, Caijin Zhou, Shoujian Zheng, Jianxiang Lu, Jiabin Huang, Comi Chen, Junshu Tang, Guangzheng Xu, Jiale Tao, Hongmei Wang, Donghao Li, Wenqing Yu, Senbo Wang, Zhimin Li, Yetshuan Shi, Haoyu Yang, Yukun Wang, Wenxun Dai, Jiaqi Li, Linqing Wang, Qixun Wang, Zhiyong Xu, Yingfang Zhang, Jiangfeng Xiong, Weijie Kong, Chao Zhang, Hongxin Zhang, Qiaoling Zheng, Weiting Guo, Xinchi Deng, Yixuan Li, Renjia Wei, Yulin Jian, Duojun Huang, Xuhua Ren, Sihuan Lin, Yifu Sun, Yuan Zhou, Joey Wang, Qin Lin, Jingmiao Yu, Jihong Zhang, Caesar Zhong, Di Wang, Yuhong Liu, Linus, Jie Jiang, Longhuang Wu, Shuai Shao, Qinglin Lu

cs.AI

摘要

智能游戏创作标志着游戏开发领域的一次革命性进步，它利用生成式人工智能动态生成并优化游戏内容。尽管生成模型已取得显著进展，但高质量游戏资产（包括图像与视频）的全面合成仍是一个充满挑战的前沿领域。为了创造出既符合玩家偏好又能大幅提升设计师效率的高保真游戏内容，我们推出了旨在革新智能游戏生产的创新项目——HunYuan-Game。HunYuan-Game包含两大核心分支：图像生成与视频生成。图像生成部分基于包含数十亿游戏图像的庞大数据集，开发了一系列专为游戏场景定制的图像生成模型：(1) 通用文本到图像生成。(2) 游戏视觉效果生成，涵盖基于文本到效果及参考图像的视觉效果生成。(3) 针对角色、场景及游戏视觉效果的透明图像生成。(4) 基于草图、黑白图像及白模的游戏角色生成。视频生成部分则依托于数百万游戏与动漫视频的全面数据集，构建了五大核心算法模型，每个模型均针对游戏开发中的关键痛点，并具备对多样化游戏视频场景的强大适应能力：(1) 图像到视频生成。(2) 360度A/T姿态角色视频合成。(3) 动态插画生成。(4) 生成式视频超分辨率。(5) 交互式游戏视频生成。这些图像与视频生成模型不仅展现出高水平的艺术表现力，还深度融合了领域专业知识，形成了对多样化游戏与动漫艺术风格的系统性理解。

English

Intelligent game creation represents a transformative advancement in game development, utilizing generative artificial intelligence to dynamically generate and enhance game content. Despite notable progress in generative models, the comprehensive synthesis of high-quality game assets, including both images and videos, remains a challenging frontier. To create high-fidelity game content that simultaneously aligns with player preferences and significantly boosts designer efficiency, we present Hunyuan-Game, an innovative project designed to revolutionize intelligent game production. Hunyuan-Game encompasses two primary branches: image generation and video generation. The image generation component is built upon a vast dataset comprising billions of game images, leading to the development of a group of customized image generation models tailored for game scenarios: (1) General Text-to-Image Generation. (2) Game Visual Effects Generation, involving text-to-effect and reference image-based game visual effect generation. (3) Transparent Image Generation for characters, scenes, and game visual effects. (4) Game Character Generation based on sketches, black-and-white images, and white models. The video generation component is built upon a comprehensive dataset of millions of game and anime videos, leading to the development of five core algorithmic models, each targeting critical pain points in game development and having robust adaptation to diverse game video scenarios: (1) Image-to-Video Generation. (2) 360 A/T Pose Avatar Video Synthesis. (3) Dynamic Illustration Generation. (4) Generative Video Super-Resolution. (5) Interactive Game Video Generation. These image and video generation models not only exhibit high-level aesthetic expression but also deeply integrate domain-specific knowledge, establishing a systematic understanding of diverse game and anime art styles.