Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks

3D Morphable Model (3DMM) based methods have achieved great success in recovering 3D face shapes from single-view images. However, the facial textures recovered by such methods lack the fidelity as exhibited in the input images. Recent work demonstrates high-quality facial texture recovering with generative networks trained from a large-scale database of high-resolution UV maps of face textures, which is hard to prepare and not publicly available. In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database. The main idea is to refine the initial texture generated by a 3DMM based method with facial details from the input image. To this end, we propose to use graph convolutional networks to reconstruct the detailed colors for the mesh vertices instead of reconstructing the UV map. Experiments show that our method can generate high-quality results and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.Comment: Accepted to CVPR 2020. The source code is available at https://github.com/FuxiCV/3D-Face-GCN

Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks

Generating realistic 3D faces is of high importance for computer graphics and computer vision applications. Generally, research on 3D face generation revolves around linear statistical models of the facial surface. Nevertheless, these models cannot represent faithfully either the facial texture or the normals of the face, which are very crucial for photo-realistic face synthesis. Recently, it was demonstrated that Generative Adversarial Networks (GANs) can be used for generating high-quality textures of faces. Nevertheless, the generation process either omits the geometry and normals, or independent processes are used to produce 3D shape information. In this paper, we present the first methodology that generates high-quality texture, shape, and normals jointly, which can be used for photo-realistic synthesis. To do so, we propose a novel GAN that can generate data from different modalities while exploiting their correlations. Furthermore, we demonstrate how we can condition the generation on the expression and create faces with various facial expressions. The qualitative results shown in this paper are compressed due to size limitations, full-resolution results and the accompanying video can be found in the supplementary documents. The code and models are available at the project page: https://github.com/barisgecer/TBGAN.Comment: Check project page: https://github.com/barisgecer/TBGAN for the full resolution results and the accompanying vide

Dense 3D face decoding over 2500FPS: Joint texture and shape convolutional mesh decoders

3D Morphable Models (3DMMs) are statistical models that represent facial texture and shape variations using a set of linear bases and more particular Principal Component Analysis (PCA). 3DMMs were used as statistical priors for reconstructing 3D faces from images by solving non-linear least square optimization problems. Recently, 3DMMs were used as generative models for training non-linear mappings (i.e., regressors) from image to the parameters of the models via Deep Convolutional Neural Networks (DCNNs). Nev- ertheless, all of the above methods use either fully con- nected layers or 2D convolutions on parametric unwrapped UV spaces leading to large networks with many parame- ters. In this paper, we present the first, to the best of our knowledge, non-linear 3DMMs by learning joint texture and shape auto-encoders using direct mesh convolutions. We demonstrate how these auto-encoders can be used to train very light-weight models that perform Coloured Mesh Decoding (CMD) in-the-wild at a speed of over 2500 FPS

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