# Face Generation

ID-to-3D is an innovative method that can generate 3D head models with identity and text guidance from a random, everyday photo. It features separable expressions. This method is combinatorial, using a task-specific 2D diffusion model as an optimized prior. By extending the base model and adding lightweight expression and identity-aware architectures, a 2D prior is created for geometry and texture generation, and refined by fine-tuning only 0.2% of the available training parameters. Combining powerful facial identity embeddings and neural representations, this method not only accurately reconstructs facial features but also reconstructs accessories and hair, and provides render-ready assets suitable for games and remote presentations.

This Person Does Not Exist is an AI-powered face generation tool. It utilizes StyleGAN3 technology to create realistic and unique face images. Every generated face is computer-created and does not exist in reality. Users can choose to generate male, female, or random face images, with a maximum of 8 faces generated per request. These face images have high-definition quality at a resolution of 1024x1024, suitable for a variety of uses including design projects, presentations, or creative activities.

GFPGAN is a practical facial restoration algorithm that can be used to repair old photos or generate faces. It produces higher quality results with more details, which can be useful for facial recognition. The model runs on Nvidia T4 GPU hardware, and predictions are typically completed within 17 seconds. If GFPGAN is helpful to you, please give the Github Repo a like and recommend it to your friends.