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

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.