GGHead: Fast and Generalizable 3D Gaussian Heads

Understand

Learning 3D head priors from large 2D image collections is an important step towards high-quality 3D-aware human modeling.

• A core requirement is an efficient architecture that scales well to large-scale datasets and large image resolutions.
• Unfortunately, existing 3D GANs struggle to scale to generate samples at high resolutions due to their relatively slow train and render speeds, and typically have to rely on 2D superresolution networks at the expense of global 3D consistency.
• To address these challenges, we propose Generative Gaussian Heads (GGHead), which adopts the recent 3D Gaussian Splatting representation within a 3D GAN framework.