Extreme 3D Face Reconstruction: Seeing Through Occlusions

Existing single view, 3D face reconstruction methods can produce beautifully detailed 3D results, but typically only for near frontal, unobstructed viewpoints. We describe a system designed to provide detailed 3D reconstructions of faces viewed under extreme conditions, out of plane rotations, and occlusions. Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e.g., wrinkles). We estimate a coarse 3D face shape which acts as a foundation and then separately layer this foundation with details represented by a bump map. We show how a deep convolutional encoder-decoder can be used to estimate such bump maps. We further show how this approach naturally extends to generate plausible details for occluded facial regions. We test our approach and its components extensively, quantitatively demonstrating the invariance of our estimated facial details. We further provide numerous qualitative examples showing that our method produces detailed 3D face shapes in viewing conditions where existing state of the art often break down.Comment: Accepted to CVPR'18. Previously titled: "Extreme 3D Face Reconstruction: Looking Past Occlusions

Fully Automatic Expression-Invariant Face Correspondence

We consider the problem of computing accurate point-to-point correspondences among a set of human face scans with varying expressions. Our fully automatic approach does not require any manually placed markers on the scan. Instead, the approach learns the locations of a set of landmarks present in a database and uses this knowledge to automatically predict the locations of these landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. To accurately fit the expression of the template to the expression of the scan, we use as template a blendshape model. Our algorithm was tested on a database of human faces of different ethnic groups with strongly varying expressions. Experimental results show that the obtained point-to-point correspondence is both highly accurate and consistent for most of the tested 3D face models

Shape: A 3D Modeling Tool for Astrophysics

We present a flexible interactive 3D morpho-kinematical modeling application for astrophysics. Compared to other systems, our application reduces the restrictions on the physical assumptions, data type and amount that is required for a reconstruction of an object's morphology. It is one of the first publicly available tools to apply interactive graphics to astrophysical modeling. The tool allows astrophysicists to provide a-priori knowledge about the object by interactively defining 3D structural elements. By direct comparison of model prediction with observational data, model parameters can then be automatically optimized to fit the observation. The tool has already been successfully used in a number of astrophysical research projects.Comment: 13 pages, 11 figures, accepted for publication in the "IEEE Transactions on Visualization and Computer Graphics

Combining Dense Nonrigid Structure from Motion and 3D Morphable Models for Monocular 4D Face Reconstruction

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record Monocular 4D face reconstruction is a challenging problem, especially in the case that the input video is captured under unconstrained conditions, i.e. "in the wild". The majority of the state-of-the-art approaches build upon 3D Morphable Modelling (3DMM), which has been proven to be more robust than model-free approaches such as Shape from Shading (SfS) or Structure from Motion (SfM). While offering visually plausible shape reconstruction results that resemble real faces, 3DMMs adhere to the model space learned from exemplar faces during the training phase, often yielding facial reconstructions that are excessively smooth and look too similar even across captured faces with completely different facial characteristics. This is due to the fact that 3DMMs are typically used as hard constraints on the reconstructed 3D shape. To overcome these limitations, in this paper we propose to combine 3DMMs with Dense Nonrigid Structure from Motion (DNSM), which is much less robust but has the potential of reconstructing fine details and capturing the subject-specific facial characteristics of every input. We effectively combine the best of both worlds by introducing a novel dense variational framework, which we solve efficiently by designing a convex optimisation strategy. In contrast to previous methods, we incorporate 3DMM as a soft constraint, penalizing both departure of reconstructed faces from the 3DMM subspace and variation of the identity component of the 3DMM over different frames of the input video. As demonstrated in qualitative and quantitative experiments, our method is robust, accurately estimates the 3D facial shape over time and outperforms other state-of-the-art methods of 4D face reconstruction