High Quality Facial Surface and Texture Synthesis via Generative Adversarial Networks

ECCVW 2018 pp. 498-513

doi:10.1007/978-3-030-11015-4_36 /eccvw/2018/slossberg2018eccvw-high/

Abstract

In the past several decades, many attempts have been made to model synthetic realistic geometric data. The goal of such models is to generate plausible 3D geometries and textures. Perhaps the best known of its kind is the linear 3D morphable model (3DMM) for faces. Such models can be found at the core of many computer vision applications such as face reconstruction, recognition and authentication to name just a few.

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Text

Slossberg et al. "High Quality Facial Surface and Texture Synthesis via Generative Adversarial Networks." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11015-4_36

Markdown

[Slossberg et al. "High Quality Facial Surface and Texture Synthesis via Generative Adversarial Networks." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/slossberg2018eccvw-high/) doi:10.1007/978-3-030-11015-4_36

BibTeX

@inproceedings{slossberg2018eccvw-high,
  title     = {{High Quality Facial Surface and Texture Synthesis via Generative Adversarial Networks}},
  author    = {Slossberg, Ron and Shamai, Gil and Kimmel, Ron},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2018},
  pages     = {498-513},
  doi       = {10.1007/978-3-030-11015-4_36},
  url       = {https://mlanthology.org/eccvw/2018/slossberg2018eccvw-high/}
}