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Title: The construction and application of large scale 3D facial models
Author: Booth, James
ISNI:       0000 0004 7427 7408
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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3D Morphable Models (3DMMs) are powerful statistical models of the 3D shape and texture of the human face, and algorithms based around them are presently state of the art for recovering the 3D geometry and appearance of a face from an image. Even so, the applications of such techniques are gated by a number of factors. Firstly, existing techniques for constructing 3DMMs require manual intervention, limiting the amount of training data that can be used. As 3DMM algorithms can only recover solutions present in the span of the statistical model used, this places a fundamental limit on reconstruction quality to the confines of face models trained from smaller cohorts of data. Secondly, existing approaches for 3D reconstruction from images using 3DMMs are either fragile to real-world "in-the-wild" image effects, or limited in their ability to recover person-specific detail. Finally, there is limited work on the recovery of 3D geometry from "in-the-wild" video sequences of an individual, a very common modality of data in 2017. This thesis takes steps forwards in all three of these domains. A novel pipeline for 3DMM construction is presented that is completely automated, allowing for the construction of large scale 3D Morphable Models for the first time. By using this approach on a new dataset of 10,000 facial scans, the Large Scale Facial Model (LSFM) is introduced, a new 3DMM which is shown to have far more representative power than previous facial models. It is demonstrated that this increased power translates into better performance in 3DMM applications, and a study is performed into the effects of demographic traits such as age, gender and ethnicity on 3D facial appearance. A new approach for fitting 3DMMs to images is developed which is capable of recovering shape detail without sacrificing robustness to "in-the-wild" effects. To achieve this, it is demonstrated that an "in-the-wild" texture model for a 3DMM can be learnt from a distribution of real world images. This new "in-the-wild" 3DMM benefits from a simple but effective cost function that can be reliably optimised even in the case of challenging facial images. Finally, videos of people are given specific treatment for the "in-the-wild" 3DMM, leading to an optimal solution for 3D face tracking in this common data modality.
Supervisor: Zafeiriou, Stefanos Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral