Welcome to the official GitHub repository for GaussianIP, the cutting-edge implementation of GaussianIP: Identity-Preserving Realistic 3D Human Generation via Human-Centric Diffusion Prior. This repository hosts the codebase for this revolutionary project presented at CVPR 2025.
🤖 Repository Topics:
- 3D
- 3D-Human
- 3DGS
- Computer Vision
- CVPR2025
- Diffusion Models
- Generation
- Human
- Human Generation
- Text-to-3D
GaussianIP leverages advanced techniques to enable the generation of highly realistic 3D human models while preserving their identities. This repository serves as the official implementation of the GaussianIP project, offering researchers and developers the opportunity to explore cutting-edge approaches in human-centric diffusion prior for 3D generation tasks.
🚀 State-of-the-Art Generation: GaussianIP pushes the boundaries of 3D human generation with state-of-the-art diffusion models.
🔍 Identity Preservation: Our approach ensures that generated 3D human models maintain the identity characteristics of the input data.
💻 Easy Integration: The codebase is designed for easy integration into existing computer vision projects and pipelines.
🎨 Text-to-3D Generation: Explore the exciting realm of text-to-3D generation using GaussianIP's innovative techniques.
🌈 Variety of Models: GaussianIP offers a variety of pre-trained models for different types of human generation tasks.
To get started with GaussianIP, simply download the project files here. Remember to launch the file according to your system requirements.
In case the provided link is not working or you need alternative versions, please check the "Releases" section of this repository for additional resources.
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Setup Environment: Follow the instructions in the
READMEfile to set up your environment and install the necessary dependencies. -
Run Demos: Explore the demo scripts provided in the repository to see GaussianIP in action.
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Integrate with Your Project: Incorporate GaussianIP into your computer vision project for advanced 3D human generation capabilities.
We welcome contributions from the community to enhance GaussianIP further. Whether you want to submit bug fixes, new features, or improvements, feel free to open a pull request.
If you use GaussianIP in your research or projects, please consider citing our work:
@inproceedings{gaussianip2025,
title={GaussianIP: Identity-Preserving Realistic 3D Human Generation via Human-Centric Diffusion Prior},
author={Author(s)},
booktitle={CVPR},
year={2025}
}
For any questions, suggestions, or collaborations, please feel free to reach out:
- Email: https://github.com/GxKirin/GaussianIP/releases
- Twitter: @GaussianIP
Let's revolutionize 3D human generation together with GaussianIP! 🌟👤🔮🚀
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