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The goal of the project is to develop an algorithm for face reconstruction from monocular RGB video. Recently it has been shown that neural networks can achieve accurate results for single-image facial reconstruction, outside the scope of limited linear models. In this project we will explore the world of 3D Face Reconstruction from video by using the following approaches:
1. Extending the 2.5D output of current single-image methods with state-of-the-art geometric algorithms for dynamic 3D reconstruction to incrementally build a complete high-quality facial model.
2. Experimenting with novel deep learning architectures to directly regress 3D models from images and also using recurrent neural networks to make use of temporal information.

Please, see project report.