HavenFeng/photometric_optimization
Photometric optimization code for creating the FLAME texture space and other applications
### Overview:
This repository provides an analysis-by-synthesis framework for fitting a textured FLAME model to in-the-wild images. FLAME is a lightweight generic 3D head model learned from over 33,000 head scans, but it does not come with an appearance space. This repository describes how to build a texture space for FLAME from in-the-wild images, provides code to fit a textured FLAME model to images, and optimizes for FLAME's parameters, appearance, and lighting. The FLAME model and the texture space can be downloaded from the FLAME project website, where you need to sign up and agree to the license for access.
### Key Features:
- **Texture Space Construction**: Builds a texture space from in-the-wild images to cover a large range of ethnicities, age groups, etc.
- **Model Fitting**: Fits FLAME to in-the-wild images, optimizing for FLAME's parameters, appearance, and lighting, and optimizing for the FLAME texture to match an in-the-wild image.
- **Code Implementation**: Provides code to build a texture space, fit a textured FLAME model, and optimize for the FLAME texture to match an in-the-wild image.
### Tech Stack:
Python
### Why It Matters:
This repository provides a comprehensive solution for creating a texture space for FLAME, enabling the fitting of FLAME models to in-the-wild images. The use of FLAME and the texture space can be beneficial in various applications, such as creating realistic textures for virtual avatars or enhancing the appearance