A study on recognizing non-artistic face sketches

et al. 2019

Int J Comput Vis

Section: Neural Generative Modelsmentioning

confidence: 99%

Identity-Preserving Face Recovery from Stylized Portraits

et al. 2019

Int J Comput Vis

“…Sketch from/to photograph synthesis is explored in [24,49,37,32,30,40]. When compared to sketch-to-face synthesis, viewed as a specific case of face recovery, our unified framework is able to process much more complex styles.…”

Section: Deep Generative Modelsmentioning

confidence: 99%

Recovering Faces From Portraits with Auxiliary Facial Attributes

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

et al. 2019

Recovering a photorealistic face from an artistic portrait is a challenging task since crucial facial details are often distorted or completely lost in artistic compositions. To handle this loss, we propose an Attribute-guided Face Recovery from Portraits (AFRP) that utilizes a Face Recovery Network (FRN) and a Discriminative Network (DN). FRN consists of an autoencoder with residual block-embedded skip-connections and incorporates facial attribute vectors into the feature maps of input portraits at the bottleneck of the autoencoder. DN has multiple convolutional and fullyconnected layers, and its role is to enforce FRN to generate authentic face images with corresponding facial attributes dictated by the input attribute vectors. For the preservation of identities, we impose the recovered and ground-truth faces to share similar visual features. Specifically, DN determines whether the recovered image looks like a real face and checks if the facial attributes extracted from the recovered image are consistent with given attributes. Our method can recover photorealistic identity-preserving faces with desired attributes from unseen stylized portraits, artistic paintings, and hand-drawn sketches. On large-scale synthesized and sketch datasets, we demonstrate that our face recovery method achieves state-of-the-art results.

“…Moreover, there exist several methods to synthesize sketches from photographs (and vice versa) [28,50,39,36]. While sketch-to-face synthesis is a related problem, our unified framework can work with various more complex styles.…”

Section: Neural Generative Modelsmentioning

confidence: 99%

Identity-Preserving Face Recovery from Portraits

2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

Hartley

et al. 2018

Recovering the latent photorealistic faces from their artistic portraits aids human perception and facial analysis. However, a recovery process that can preserve 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) 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 role of the DN is to enforce recovered faces 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. We evaluate our method on a large-scale synthesized dataset of real and stylized face pairs and attain state of the art results. In addition, our method can recover photorealistic faces from previously unseen stylized portraits, original paintings and human-drawn sketches.