GameGen-O
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Gamegen O
Overview :
GameGen-O is the first diffusion transformation model customized for generating open-world video games. By simulating various features of game engines, such as innovative characters, dynamic environments, complex actions, and diverse events, it enables high-quality, open-domain generation. Additionally, it offers interactive controllability, which allows for gameplay simulation. The development of GameGen-O involved extensive data collection and processing from the ground up, including the construction of the first open-world video game dataset (OGameData) and efficient sorting, scoring, filtering, and decoupling of titles through a proprietary data pipeline. This robust and comprehensive OGameData serves as the foundation for the model training process.
Target Users :
GameGen-O is designed for game developers, AI researchers, and professionals interested in generative models. It assists developers in quickly generating game content, provides new research tools for AI researchers, and offers professionals innovative ways to create interactive game content.
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Use Cases
Developers use GameGen-O to create open-world game scenes with dynamic environments and complex actions.
AI researchers leverage the OGameData dataset for generating video game content and researching interactive control.
Game designers quickly prototype and test new game concepts and mechanics using GameGen-O.
Features
High-quality open-domain video game generation: Simulate game engine features to generate innovative characters, dynamic environments, and more.
Interactive controllability: Allow users to generate and control game content based on multi-modal structural instructions.
Two-phase training process: Foundation model pre-training and instruction fine-tuning to enhance the model’s generation and interaction capabilities.
OGameData dataset: Collect and build the first open-world video game dataset to provide a basis for model training.
Text-to-video generation and video continuation: Utilize a masked attention mechanism to achieve text-to-video generation and video continuation.
Multi-modal input control: InstructNet accepts various inputs, including structured text, action signals, and video prompts, to control content generation.
How to Use
Visit the GameGen-O GitHub page to access the models and datasets.
Read the documentation to understand how the model works and how to train and fine-tune it.
Download and install the necessary software and libraries to run the GameGen-O model.
Train the model using the OGameData dataset, or directly utilize pre-trained models for game content generation.
Control the generated content by providing structured text, action signals, or video prompts.
Adjust the model parameters as needed to optimize the generated game content.
Integrate the generated content into the game development workflow or use it for research and prototyping.
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