Hunyuan-Game: Industrial-grade Intelligent Game Creation Model

2025-05-20

AI Summary

This study addresses the low efficiency and stylistic inconsistency in generating high-quality multimodal content for game development. To this end, we propose a domain-specific, full-stack generative architecture tailored for industrial-scale production. Methodologically, we construct nine specialized generative models covering both image and video modalities—including transparent-image synthesis, 360° pose-controllable avatar video generation, and dynamic illustration synthesis—supporting multimodal inputs such as text, sketches, reference images, and pose sequences. Leveraging a large-scale dataset comprising billions of game-related images and millions of game/anime videos, our framework integrates diffusion modeling, conditional control encoding, cross-modal alignment, generative super-resolution, and interactive temporal modeling. Experimental results demonstrate state-of-the-art performance across multiple tasks, enabling end-to-end generation of characters, visual effects, environments, and animations—thereby significantly improving artistic production efficiency and ensuring consistent aesthetic style.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• Synthesizing high-quality game assets (images and videos)
• Aligning game content with player preferences efficiently
• Enhancing designer productivity via AI-generated game elements

Innovation

• Customized image generation models for game scenarios
• Five core algorithmic models for video generation
• Integration of domain-specific knowledge in game art