Hunyuan Gamecraft

High-Dynamic Interactive Game Video Generation with Hybrid History Condition

What is Hunyuan Gamecraft?

Hunyuan Gamecraft is a novel framework developed by Tencent for creating high-dynamic interactive game video generation. This framework represents a significant advancement in video synthesis technology, specifically designed to address the limitations of current methods in dynamics, physical realism, long-term consistency, and efficiency within gaming environments.

The system enables fine-grained action control by unifying standard keyboard and mouse inputs into a shared camera representation space. This innovation facilitates smooth interpolation between various camera and movement operations, creating more natural and responsive gameplay experiences. The framework employs a hybrid history-conditioned training strategy that extends video sequences autoregressively while preserving crucial game scene information.

Trained on a comprehensive dataset comprising over one million gameplay recordings across more than 100 AAA games, Hunyuan Gamecraft ensures broad coverage and diversity. The model was then fine-tuned on a carefully annotated synthetic dataset to enhance precision and control, resulting in significantly improved visual fidelity, realism, and action controllability compared to existing interactive video generation models.

Overview of Hunyuan Gamecraft

Feature	Description
Developer	Tencent Research Team
Category	Interactive Game Video Generation Framework
Primary Function	High-dynamic Interactive Video Generation
Training Dataset	Over 1 million gameplay recordings from 100+ AAA games
Control Method	Keyboard and mouse input unified in camera space
Research Paper	arXiv:2506.17201
Model Version	Hunyuan-GameCraft-1.0

Technical Architecture

The architecture of Hunyuan Gamecraft consists of several key components that work together to generate interactive game videos. Given a reference image and corresponding prompt, along with keyboard or mouse signals, the system transforms these inputs into a continuous camera space representation.

The framework employs a lightweight action encoder to process input camera trajectories. Action and image features are combined after the patchify process, creating a unified representation that enables precise control over video generation. For long video extension capabilities, the system implements a variable mask indicator where binary values distinguish between history frames and predicted frames.

The hybrid history condition mechanism ensures that the generated videos maintain consistency across extended sequences while preserving important scene information. This approach addresses one of the major challenges in interactive video generation: maintaining coherence and quality over longer durations.

Key Features of Hunyuan Gamecraft

Unified Input Control System
Transforms standard keyboard and mouse inputs into a shared camera representation space, enabling smooth interpolation between different camera and movement operations for natural gameplay experiences.
Hybrid History-Conditioned Training
Extends video sequences autoregressively while preserving game scene information, ensuring long-term consistency and coherence in generated content.
Model Distillation for Efficiency
Reduces computational overhead while maintaining consistency across long temporal sequences, making the framework suitable for real-time deployment in complex interactive environments.
Comprehensive Training Dataset
Trained on over one million gameplay recordings from more than 100 AAA games, ensuring broad coverage and diversity across different gaming scenarios and art styles.
Superior Visual Fidelity
Delivers enhanced visual quality and realism through carefully curated game scene data that significantly improves action controllability and scene coherence.
Multiple Perspective Support
Capable of generating both first-person and third-person gaming scenarios with natural controls, adapting to different gameplay styles and requirements.

Performance Capabilities

Control Accuracy

Demonstrates superior control accuracy under single-action control compared to other interactive video methods across multiple game scenarios and art styles. The system accurately interprets user inputs and translates them into corresponding video movements.

Long-term Consistency

Maintains visual consistency over extended video sequences, ensuring that generated content remains coherent and believable throughout longer gameplay sessions.

History Preservation

Effectively preserves original scene information after significant movement, maintaining 3D consistency and scene coherence essential for immersive game experiences.

Dynamic Performance

Excels in handling complex trajectories and multiple sequential operational signals, focusing on quality, continuity, and consistency in overall output generation.

Application Scenarios

Rural Landscapes

Generate immersive countryside environments featuring traditional windmills, golden fields, and natural landscapes under various weather conditions. Perfect for open-world exploration games.

Urban Environments

Create vibrant city scenes with colorful European-style buildings, tram tracks, and modern skyscrapers. Ideal for city-building simulations and urban adventure games.

Natural Settings

Design serene landscapes featuring rivers, lush green fields, and dramatic skies. Suitable for relaxation games and nature exploration experiences.

Action Scenarios

Generate tactical environments with sniper positions overlooking mountainous landscapes, perfect for strategic and action gaming experiences.

Pros and Cons

Pros

Superior control accuracy across diverse scenarios
Excellent long-term consistency in video generation
Effective history preservation during scene transitions
Comprehensive training on diverse gaming content
Real-time deployment capability through model distillation
Support for both first-person and third-person perspectives
Natural interpolation between camera movements
High visual fidelity and realism

Cons

Requires significant computational resources for training
Performance depends on quality of input prompts
May require fine-tuning for specific game genres
Complex setup process for implementation
Limited to gaming-specific scenarios

How to Use Hunyuan Gamecraft

Step 1: Environment Setup

Install the required dependencies and download the model weights. Ensure you have sufficient GPU memory (minimum 24GB, recommended 80GB) for optimal performance.

Step 2: Input Preparation

Prepare your reference image and corresponding text prompt describing the desired scene. Define the action sequence using keyboard inputs (W, A, S, D) or mouse movements.

Step 3: Camera Configuration

Configure camera parameters including movement speed and trajectory. The system automatically transforms these inputs into the unified camera representation space.

Step 4: Generation Process

Execute the generation process with specified parameters such as video size, inference steps, and flow shift values. Monitor the progress as the model generates the interactive video sequence.

Step 5: Output Review

Review the generated video for quality, consistency, and adherence to input commands. The output can be saved in standard video formats for further use or integration.