Hunyuan-Game: Industrial-grade Intelligent Game Creation Model

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; Junkun Yuan; Zhengguang Zhou; Jiaxiang Cheng; Bing Ma; Shirui Huang; Jiawang Bai; Chao Li; Sihuan Lin; Yifu Sun; Yuan Zhou; Joey Wang; Qin Lin; Tianxiang Zheng; Jingmiao Yu; Jihong Zhang; Caesar Zhong; Di Wang; Yuhong Liu; Linus; Jie Jiang; Longhuang Wu; Shuai Shao; Qinglin Lu

arXiv · 2505.14135

Hunyuan-Game: Industrial-grade Intelligent Game Creation Model

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Abstract

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.

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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, Junkun Yuan, Zhengguang Zhou, Jiaxiang Cheng, Bing Ma, Shirui Huang, Jiawang Bai, Chao Li, Sihuan Lin, Yifu Sun, Yuan Zhou, Joey Wang, Qin Lin, Tianxiang Zheng, Jingmiao Yu, Jihong Zhang, Caesar Zhong, Di Wang, Yuhong Liu, Linus, Jie Jiang, Longhuang Wu, Shuai Shao, Qinglin Lu. 2025-05-28. Hunyuan-Game: Industrial-grade Intelligent Game Creation Model. https://arxiv.org/abs/2505.14135

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