  Recent face generation methods have tried to synthesize faces based on the
given contour condition, like a low-resolution image or sketch. However, the
problem of identity ambiguity remains unsolved, which usually occurs when the
contour is too vague to provide reliable identity information (e.g., when its
resolution is extremely low). Thus feasible solutions of image restoration
could be infinite. In this work, we propose a novel framework that takes the
contour and an extra image specifying the identity as the inputs, where the
contour can be of various modalities, including the low-resolution image,
sketch, and semantic label map. Concretely, we propose a novel dual-encoder
architecture, in which an identity encoder extracts the identity-related
feature, accompanied by a main encoder to obtain the rough contour information
and further fuse all the information together. The encoder output is
iteratively fed into a pre-trained StyleGAN generator until getting a
satisfying result. To the best of our knowledge, this is the first work that
achieves identity-guided face generation conditioned on multi-modal contour
images. Moreover, our method can produce photo-realistic results with
1024$\times$1024 resolution.
