Face Reconstruction - A Learning Approach

Speaker:
Elad Richardson, M.Sc. Thesis Seminar
Date:
Thursday, 2.2.2017, 11:30
Place:
Taub 337
Advisor:
Prof. R. Kimmel

Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications in computer vision and graphics. We propose to leverage the power of convolutional neural networks (CNNs) to produce highly detailed face reconstruction directly from a single image. For this purpose, we introduce an end-to-end CNN framework which constructs the shape in a coarse-to-fine fashion. The proposed architecture is composed of two main blocks, a network which recovers the coarse facial geometry (CoarseNet), followed by a CNN which refines the facial features of that geometry (FineNet). To alleviate the lack of training data for face reconstruction, we train our model using synthetic data as well as unlabeled facial images collected from the internet. The proposed model successfully recovers facial geometries from real images, even for faces with extreme expressions and under varying lighting conditions.

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