Feed-forward 3D reconstruction from multi-view images can match or exceed optimization-based methods while being much faster, and UV-parameterization lets you train with many high-resolution views without memory explosion.
HeadsUp reconstructs detailed 3D head models from multiple camera views using an efficient neural network that compresses images into a compact representation, then decodes them into 3D Gaussians (mathematical shapes). The method scales to thousands of subjects and works on new people without extra optimization, enabling applications like generating new identities and animating expressions.