DCFace: Synthetic Face Generation with Dual Condition Diffusion Model. (arXiv:2304.07060v1 [cs.CV])

Generating synthetic datasets for training face recognition models is
challenging because dataset generation entails more than creating high fidelity
images. It involves generating multiple images of same subjects under different
factors (\textit{e.g.}, variations in pose, illumination, expression, aging and
occlusion) which follows the real image conditional distribution. Previous
works have studied the generation of synthetic datasets using GAN or 3D models.
In this work, we approach the problem from the aspect of combining subject
appearance (ID) and external factor (style) …

3d models aging arxiv dataset dataset generation datasets diffusion diffusion model distribution face face recognition fidelity gan image images multiple recognition synthetic training work