Large pose 3D face reconstruction from a single image via direct volumetric CNN regression

3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodological challenges such as establishing dense correspondences across large facial poses, expressions, and non-uniform illumination. In general these methods require complex and inefficient pipelines for model building and fitting. In this work, we propose to address many of these limitations by training a Convolutional Neural Network (CNN) on an appropriate dataset consisting of 2D images and 3D facial models or scans. Our CNN works with just a single 2D facial image, does not require accurate alignment nor establishes dense correspondence between images, works for arbitrary facial poses and expressions, and can be used to reconstruct the whole 3D facial geometry (including the non-visible parts of the face) bypassing the construction (during training) and fitting (during testing) of a 3D Morphable Model. We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality, especially for the cases of large poses and facial expressions. Testing code will be made available online, along with pre-trained models http://aaronsplace.co.uk/papers/jackson2017reconComment: 10 pages, ICCV 201

Conversion of 2D Image into 3D and Face Recognition Based Attendance System

ABSTRACT: The world is being 3d and the 3d imaging having more advantages over 2d,presently there are attendance system based on fingerprint recognition but that are having some advantages like the surface should be dust free and also we have the contact with the sensor so that finger should have to keep properly. There are also 2D face recognition based attendance system. To overcome the problems in 2d i.e. Lighting variations, Expression variations, variations 3D face recognition is used. We propose a system that takes the attendance of the student using Conversion of 2d images into 3d And then face face detection and recognition . We are using two 2d images taken from two different camera and that is converted into 3d image using binocular disparity technique. After this image is use for the face recognition. Threedimensional face recognition (3D face recognition) is a modality of facial recognition methods in which the threedimensional geometry of the human face is used. It can also identify a face from a range of viewing angles, including a profile view. Three-dimensional data points from a face vastly improve the precision of facial recognition

Generating High-Resolution 3D Faces and Bodies Using VQ-VAE-2 with PixelSNAIL Networks on 2D Representations

Modeling and representing 3D shapes of the human body and face is a prominent field due to its applications in the healthcare, clothes, and movie industry. In our work, we tackled the problem of 3D face and body synthesis by reducing 3D meshes to 2D image representations. We show that the face can naturally be modeled on a 2D grid. At the same time, for more challenging 3D body geometries, we proposed a novel non-bijective 3D–2D conversion method representing the 3D body mesh as a plurality of rendered projections on the 2D grid. Then, we trained a state-of-the-art vector-quantized variational autoencoder (VQ-VAE-2) to learn a latent representation of 2D images and fit a PixelSNAIL autoregressive model to sample novel synthetic meshes. We evaluated our method versus a classical one based on principal component analysis (PCA) by sampling from the empirical cumulative distribution of the PCA scores. We used the empirical distributions of two commonly used metrics, specificity and diversity, to quantitatively demonstrate that the synthetic faces generated with our method are statistically closer to real faces when compared with the PCA ones. Our experiment on the 3D body geometry requires further research to match the test set statistics but shows promising results

3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses

3D-aware face generators are typically trained on 2D real-life face image datasets that primarily consist of near-frontal face data, and as such, they are unable to construct one-quarter headshot 3D portraits with complete head, neck, and shoulder geometry. Two reasons account for this issue: First, existing facial recognition methods struggle with extracting facial data captured from large camera angles or back views. Second, it is challenging to learn a distribution of 3D portraits covering the one-quarter headshot region from single-view data due to significant geometric deformation caused by diverse body poses. To this end, we first create the dataset 360{\deg}-Portrait-HQ (360{\deg}PHQ for short) which consists of high-quality single-view real portraits annotated with a variety of camera parameters (the yaw angles span the entire 360{\deg} range) and body poses. We then propose 3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator that learns a canonical 3D avatar distribution from the 360{\deg}PHQ dataset with body pose self-learning. Our model can generate view-consistent portrait images from all camera angles with a canonical one-quarter headshot 3D representation. Our experiments show that the proposed framework can accurately predict portrait body poses and generate view-consistent, realistic portrait images with complete geometry from all camera angles

A methodology for feature based 3D face modelling from photographs

In this paper, a new approach to modelling 3D faces based on 2D images is introduced. Here 3D faces are created using two photographs from which we extract facial features based on image manipulation techniques. Through the image manipulation techniques we extract the crucial feature lines of the face in two views. These are then used in modifying a template base mesh which is created in 3D. This base mesh, which has been designed by keeping facial animation in mind, is then subdivided to provide the level of detail required. The methodology, as it stands, is semi-automatic whereby our goal is to automate this process in order to provide an inexpensive and expedient way of producing realistic face models intended for animation purposes. Thus, we show how image manipulation techniques can be used to create binary images which can in turn be used in manipulating a base mesh that can be adapted to a given facial geometry. In order to explain our approach more clearly we discuss a series of examples where we create 3D facial geometry of individuals given the corresponding image data

Efficient Dense 3D Reconstruction Using Image Pairs

The 3D reconstruction of a scene from 2D images is an important topic in the _x000C_eld of Computer Vision due to the high demand in various applications such as gaming, animations, face recognition, parts inspections, etc. The accuracy of a 3D reconstruction is highly dependent on the accuracy of the correspondence matching between the images. For the purpose of high accuracy of 3D reconstruction system using just two images of the scene, it is important to _x000C_nd accurate correspondence between the image pairs. In this thesis, we implement an accurate 3D reconstruction system from two images of the scene at di_x000B_erent orientation using a normal digital camera. We use epipolar geometry to improvise the performance of the initial coarse correspondence matches between the images. Finally we calculate the reprojection error of the 3D reconstruction system before and after re_x000C_ning the correspondence matches using the epipolar geometry and compare the performance between them. Even though many feature-based correspondence matching techniques provide robust matching required for 3D reconstruction, it gives only coarse correspondence matching between the images. This is not su_x000E_cient to reconstruct the detailed 3D structure of the objects. Therefore we use our improvised image matching to calculate the camera parameters and implement dense image matching using thin-plate spline interpolation, which interpolates the surface based on the initial control points obtained from coarse correspondence matches. Since the thin-plate spline interpolates highly dense points from a very few control points, the correspondence mapping between the images are not accurate. We propose a new method to improve the performance of the dense image matching using epipolar geometry and intensity based thin-plate spline interpolation. We apply the proposed method for 3D reconstruction using two images. Finally, we develop systematic evaluation for our dense 3D reconstruction system and discuss the results