AnimeGamer
A
Animegamer
Overview :
AnimeGamer is built based on a multi-modal large language model (MLLM) that can generate dynamic animation shots and character status updates, providing users with an endless anime life experience. It allows users to interact with anime characters through open-ended language instructions, creating unique adventure stories. The main advantages of this product include: dynamically generating animations for character interactions, enabling interactions between different anime, and rich game state prediction.
Target Users :
This product is suitable for anime enthusiasts and game developers who wish to experience limitless adventures in the anime world. Using AnimeGamer, users can create interactions with their favorite anime characters, expanding their imagination and creativity.
Total Visits: 485.5M
Top Region: US(19.34%)
Website Views : 39.5K
Use Cases
Users can simulate interactions between Qiqi and Pazu, creating new storylines.
Users can interact between different anime characters, such as letting Sosuke meet other anime characters.
Developers can utilize AnimeGamer's technology to create new anime games and applications.
Features
Interact with anime characters through open-ended language instructions
Generate dynamic animation shots, showcasing character movements
Update character status, including stamina, social, and entertainment values
Predict the next game state, maintaining contextual consistency
Allow users to create unique interactions across anime characters
Provide inference code for low VRAM environments
Support custom game instructions
Suitable for various anime characters
How to Use
Access the GitHub page and clone the repository: git clone https://github.com/TencentARC/AnimeGamer.git
Enter the AnimeGamer directory and create a Python environment: conda create -n animegamer python==3.10 -y
Activate the environment: conda activate animegamer
Install the required dependencies: pip install -r requirements.txt
Download the required model checkpoints and place them in the ./checkpoints directory.
Run the inference decoder to generate animation shots: python inference_Decoder.py
Modify the instructions in ./game_demo as needed to customize the game experience.
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