**GGHead: Fast and Generalizable 3D Gaussian Heads** This paper introduces GGHead, a novel method for generating and rendering high-quality 3D head representations from 2D images. GGHead leverages 3D Gaussian Splatting (3DGS) within a 3D Generative Adversarial Network (3D GAN) framework to achieve efficient and scalable 3D head generation. The key contributions of GGHead are: 1. **Efficient 3D Gaussian Representation**: GGHead parameterizes 3D Gaussian heads as UV maps, allowing for efficient prediction using powerful 2D CNN architectures. This approach simplifies the generation process and improves training stability. 2. **UV Total Variation Loss**: A novel regularization term is introduced to improve the geometric fidelity of the generated 3D heads. This loss enforces smoothness in the UV space, ensuring that neighboring pixels in the rendered image are modeled by nearby Gaussians, thus enhancing the realism of the generated geometry. 3. **Scalability and Real-Time Rendering**: GGHead can generate and render high-quality 3D heads at full 1024² resolution in real-time, making it the first method to achieve this level of performance without relying on 2D super-resolution networks. This scalability is achieved through efficient 3D Gaussian Splatting rasterization. 4. **Quality and Consistency**: GGHead matches the quality of state-of-the-art 3D GANs like EG3D and Mimic3D while being significantly faster and fully 3D consistent. Qualitative and quantitative comparisons demonstrate that GGHead produces high-quality, consistent, and detailed 3D head representations. 5. **Computational Efficiency**: GGHead is more memory-efficient and faster to train compared to other 3D GAN methods, reducing the time required for generating and rendering 3D heads at high resolutions. The paper also discusses the limitations and future work, including the potential for extending GGHead to other domains and improving the generality of the method. Overall, GGHead represents a significant advancement in the field of 3D human modeling, offering both speed and quality in 3D head generation and rendering.**GGHead: Fast and Generalizable 3D Gaussian Heads** This paper introduces GGHead, a novel method for generating and rendering high-quality 3D head representations from 2D images. GGHead leverages 3D Gaussian Splatting (3DGS) within a 3D Generative Adversarial Network (3D GAN) framework to achieve efficient and scalable 3D head generation. The key contributions of GGHead are: 1. **Efficient 3D Gaussian Representation**: GGHead parameterizes 3D Gaussian heads as UV maps, allowing for efficient prediction using powerful 2D CNN architectures. This approach simplifies the generation process and improves training stability. 2. **UV Total Variation Loss**: A novel regularization term is introduced to improve the geometric fidelity of the generated 3D heads. This loss enforces smoothness in the UV space, ensuring that neighboring pixels in the rendered image are modeled by nearby Gaussians, thus enhancing the realism of the generated geometry. 3. **Scalability and Real-Time Rendering**: GGHead can generate and render high-quality 3D heads at full 1024² resolution in real-time, making it the first method to achieve this level of performance without relying on 2D super-resolution networks. This scalability is achieved through efficient 3D Gaussian Splatting rasterization. 4. **Quality and Consistency**: GGHead matches the quality of state-of-the-art 3D GANs like EG3D and Mimic3D while being significantly faster and fully 3D consistent. Qualitative and quantitative comparisons demonstrate that GGHead produces high-quality, consistent, and detailed 3D head representations. 5. **Computational Efficiency**: GGHead is more memory-efficient and faster to train compared to other 3D GAN methods, reducing the time required for generating and rendering 3D heads at high resolutions. The paper also discusses the limitations and future work, including the potential for extending GGHead to other domains and improving the generality of the method. Overall, GGHead represents a significant advancement in the field of 3D human modeling, offering both speed and quality in 3D head generation and rendering.