CNN-Based real-time dense face reconstruction with inverse-rendered photo-realistic face images

Yudong Guo, Juyong Zhang, Jianfei Cai, Boyi Jiang, Jianmin Zheng

Research output: Contribution to journalArticleResearchpeer-review

24 Citations (Scopus)

Abstract

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data.11.All these coarse-scale and fine-scale photo-realistic face image datasets can be downloaded from https://github.com/Juyong/3DFace. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.

Original languageEnglish
Pages (from-to)1294-1307
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume41
Issue number6
DOIs
Publication statusPublished - 1 Jun 2019
Externally publishedYes

Keywords

  • 3D face dataset
  • 3D face reconstruction
  • deep learning
  • face performance capturing
  • face tracking
  • image synthesis

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