Morphable Model

Morphable models (3DMMs) are statistical models representing the shape and appearance variations of human faces and bodies, aiming to create realistic and controllable 3D avatars. Current research focuses on improving the fidelity and efficiency of these models, often integrating them with neural networks (e.g., GANs, diffusion models, neural radiance fields) and Gaussian splatting for high-quality rendering and animation, as well as incorporating semantic control via text prompts or landmarks. This work has significant implications for various fields, including virtual reality, augmented reality, computer graphics, and facial animation, enabling more realistic and customizable digital representations of humans.