  This work presents 3DPE, a practical method that can efficiently edit a face
image following given prompts, like reference images or text descriptions, in a
3D-aware manner. To this end, a lightweight module is distilled from a 3D
portrait generator and a text-to-image model, which provide prior knowledge of
face geometry and superior editing capability, respectively. Such a design
brings two compelling advantages over existing approaches. First, our method
achieves real-time editing with a feedforward network (i.e., ~0.04s per image),
over 100x faster than the second competitor. Second, thanks to the powerful
priors, our module could focus on the learning of editing-related variations,
such that it manages to handle various types of editing simultaneously in the
training phase and further supports fast adaptation to user-specified
customized types of editing during inference (e.g., with ~5min fine-tuning per
style).
