| dc.description.abstract | We present a new multi-modal face image generation
method that converts a text prompt and a visual input, such
as a semantic mask or scribble map, into a photo-realistic
face image. To do this, we combine the strengths of Generative Adversarial networks (GANs) and diffusion models
(DMs) by employing the multi-modal features in the DM
into the latent space of the pre-trained GANs. We present a
simple mapping and a style modulation network to link two
models and convert meaningful representations in feature
maps and attention maps into latent codes. With GAN inversion, the estimated latent codes can be used to generate 2D
or 3D-aware facial images. We further present a multi-step
training strategy that reflects textual and structural representations into the generated image. Our proposed network
produces realistic 2D, multi-view, and stylized face images,
which align well with inputs. We validate our method
by using pre-trained 2D and 3D GANs, and our results
outperform existing methods. Our project page is available at https://github.com/1211sh/Diffusiondriven_GAN-Inversion/. | |
