README.md

metadata

pipeline_tag: unconditional-image-generation
license: apache-2.0
tags:
  - 3D
  - Gaussian Splatting
  - GAN
  - Human Head Synthesis

CGS-GAN: 3D Consistent Gaussian Splatting GANs for High Resolution Human Head Synthesis

CGS-GAN is a novel 3D Gaussian Splatting GAN framework designed for stable training and high-quality, 3D-consistent synthesis of human heads. It addresses the challenges of existing methods that compromise 3D consistency by relying on view-conditioning. CGS-GAN introduces a multi-view regularization technique to ensure training stability and a specialized generator architecture that facilitates efficient rendering and scaling, enabling output resolutions up to $2048^2$.

• Paper: CGS-GAN: 3D Consistent Gaussian Splatting GANs for High Resolution Human Head Synthesis
• Project Page: https://fraunhoferhhi.github.io/cgs-gan/
• Repository: https://github.com/fraunhoferhhi/cgs-gan

Usage

To run inference and render results, follow the setup instructions in the official GitHub repository and use the generate_samples.py script with a pre-trained checkpoint:

python generate_samples.py --pkl path/to/network.pkl 

Optional Parameters

• --truncation_psi: Tradeoff between quality and variety (0: quality, 1: variety). Default is 0.8.
• --num_ids: Number of IDs to generate (number of rows). Default is 6.
• --radius: Radius of the camera. Default is 2.7.
• --seed: Random seed for generation. Default is 42.
• --save_dir: Directory to save the generated results. Default is "results".

Citation

Please cite our paper when using CGS-GAN in your work:

@misc{barthel2025cgsgan,
      title={CGS-GAN: 3D Consistent Gaussian Splatting GANs for High Resolution Human Head Synthesis},
      author={Florian Barthel and Wieland Morgenstern and Paul Hinzer and Anna Hilsmann and Peter Eisert},
      year={2025},
      eprint={2505.17590},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.17590},
}