Identity-preserving Face Recovery from Portraits

by   Fatemeh Shiri, et al.

Recovering the latent photorealistic faces from their artistic portraits aids human perception and facial analysis. However, recovering photorealistic faces from stylized portraits while preserving identity is challenging because the fine details of real faces can be distorted or lost in stylized images. In this paper, we present a new Identity-preserving Face Recovery from Portraits (IFRP) method to recover latent photorealistic faces from unaligned stylized portraits. Our IFRP method consists of two components: Style Removal Network (SRN) and Discriminative Network (DN). The SRN is designed to transfer feature maps of stylized images to the feature maps of the corresponding photorealistic faces. By embedding spatial transformer networks into the SRN, our method can compensate for misalignments of stylized faces automatically and output aligned realistic face images. The DN is used to enforce recovered face images to be similar to authentic faces. To ensure the identity preservation, we promote the recovered and ground-truth faces to share similar visual features via a distance measure which compares features of recovered and ground-truth faces extracted from a pre-trained VGG network. Our approach is evaluated on a large-scale synthesized dataset of real and stylized face pairs and outperforms the state-of-the-art methods. In addition, we demonstrate that our method can also recover photorealistic faces from unseen stylized portraits (unavailable in training) as well as original paintings.


page 4

page 6

page 7

page 8

page 11

page 12

page 13

page 14


Identity-preserving Face Recovery from Stylized Portraits

Given an artistic portrait, recovering the latent photorealistic face th...

Recovering Faces from Portraits with Auxiliary Facial Attributes

Recovering a photorealistic face from an artistic portrait is a challeng...

Face Destylization

Numerous style transfer methods which produce artistic styles of portrai...

DreamIdentity: Improved Editability for Efficient Face-identity Preserved Image Generation

While large-scale pre-trained text-to-image models can synthesize divers...

Controlling Memorability of Face Images

Everyday, we are bombarded with many photographs of faces, whether on so...

Recap: Detecting Deepfake Video with Unpredictable Tampered Traces via Recovering Faces and Mapping Recovered Faces

The exploitation of Deepfake techniques for malicious intentions has dri...

Preliminary Forensics Analysis of DeepFake Images

One of the most terrifying phenomenon nowadays is the DeepFake: the poss...

Please sign up or login with your details

Forgot password? Click here to reset