  Accurate 3D face reconstruction from 2D images is an enabling technology with
applications in healthcare, security, and creative industries. However, current
state-of-the-art methods either rely on supervised training with very limited
3D data or self-supervised training with 2D image data. To bridge this gap, we
present a method to generate a large-scale synthesised dataset of 250K
photorealistic images and their corresponding shape parameters and depth maps,
which we call SynthFace. Our synthesis method conditions Stable Diffusion on
depth maps sampled from the FLAME 3D Morphable Model (3DMM) of the human face,
allowing us to generate a diverse set of shape-consistent facial images that is
designed to be balanced in race and gender. We further propose ControlFace, a
deep neural network, trained on SynthFace, which achieves competitive
performance on the NoW benchmark, without requiring 3D supervision or manual 3D
asset creation. The complete SynthFace dataset will be made publicly available
upon publication.
