混元游戏：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

智能遊戲創作代表了遊戲開發領域的變革性進步，它利用生成式人工智慧來動態生成和增強遊戲內容。儘管生成模型已取得顯著進展，但高質量遊戲資產（包括圖像和影片）的全面合成仍是一個具有挑戰性的前沿領域。為了創造既符合玩家偏好又能顯著提升設計師效率的高保真遊戲內容，我們推出了創新項目「Hunyuan-Game」，旨在徹底革新智能遊戲製作。Hunyuan-Game涵蓋兩個主要分支：圖像生成和影片生成。圖像生成組件基於包含數十億遊戲圖像的龐大數據集，開發了一組專為遊戲場景定制的圖像生成模型：(1) 通用文本到圖像生成。(2) 遊戲視覺效果生成，包括基於文本和參考圖像的遊戲視覺效果生成。(3) 角色、場景和遊戲視覺效果的透明圖像生成。(4) 基於草圖、黑白圖像和白模的遊戲角色生成。影片生成組件則基於數百萬遊戲和動漫影片的綜合數據集，開發了五個核心算法模型，每個模型都針對遊戲開發中的關鍵痛點，並對多樣化的遊戲影片場景具有強大的適應性：(1) 圖像到影片生成。(2) 360度A/T姿勢角色影片合成。(3) 動態插圖生成。(4) 生成式影片超分辨率。(5) 互動式遊戲影片生成。這些圖像和影片生成模型不僅展現出高層次的美學表達，還深度融合了領域特定知識，建立了對多樣化遊戲和動漫藝術風格的系統性理解。

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.