{
  "id": "1904.03612",
  "version": "v1",
  "published": "2019-04-07T09:29:36.000Z",
  "updated": "2019-04-07T09:29:36.000Z",
  "title": "Recovering Faces from Portraits with Auxiliary Facial Attributes",
  "authors": [
    "Fatemeh Shiri",
    "Xin Yu",
    "Fatih Porikli",
    "Richard Hartley",
    "Piotr Koniusz"
  ],
  "comment": "2019 IEEE Winter Conference on Applications of Computer Vision (WACV)",
  "doi": "10.1109/WACV.2019.00049",
  "categories": [
    "cs.CV"
  ],
  "abstract": "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 fully-connected layers, and its role is to enforce FRN to generate authentic face images with corresponding facial attributes dictated by the input attribute vectors. %Leveraging on the spatial transformer networks, FRN automatically compensates for misalignments of portraits. % and generates aligned face images. 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 high-quality photorealistic faces from unaligned portraits while preserving the identity of the face images as well as it can reconstruct a photorealistic face image with a desired set of 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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-04-07T09:29:36.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "auxiliary facial attributes",
      "face image",
      "recovering faces",
      "photorealistic face",
      "face recovery method achieves state-of-the-art"
    ],
    "tags": [
      "conference paper",
      "journal article"
    ],
    "publication": {
      "publisher": "IEEE"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}