We address the problem of recovering the 3D geometry of a human face from a set of facial images in multiple views. While recent studies have shown impressive progress in 3D Morphable Model (3DMM) based facial reconstruction, the settings are mostly restricted to a single view. There is an inherent drawback in the single-view setting: the lack of reliable 3D constraints can cause unresolvable ambiguities. We in this paper explore 3DMM-based shape recovery in a different setting, where a set of multi-view facial images are given as input. A novel approach is proposed to regress 3DMM parameters from multi-view inputs with an end-to-end trainable Convolutional Neural Network (CNN). Multiview geometric constraints are incorporated into the network by establishing dense correspondences between different views leveraging a novel self-supervised view alignment loss. The main ingredient of the view alignment loss is a differentiable dense optical flow estimator that can backpropagate the alignment errors between an input view and a synthetic rendering from another input view, which is projected to the target view through the 3D shape to be inferred. Through minimizing the view alignment loss, better 3D shapes can be recovered such that the synthetic projections from one view to another can better align with the observed image. Extensive experiments demonstrate the superiority of the proposed method over other 3DMM methods.
@cite_37: This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical, morphable model of 3D faces to images. The model is learned from a set of textured 3D scans of heads. We describe the construction of the morphable model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET database.
@cite_8: We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time.
@cite_1: Detailed facial performance geometry can be reconstructed using dense camera and light setups in controlled studios. However, a wide range of important applications cannot employ these approaches, including all movie productions shot from a single principal camera. For post-production, these require dynamic monocular face capture for appearance modification. We present a new method for capturing face geometry from monocular video. Our approach captures detailed, dynamic, spatio-temporally coherent 3D face geometry without the need for markers. It works under uncontrolled lighting, and it successfully reconstructs expressive motion including high-frequency face detail such as folds and laugh lines. After simple manual initialization, the capturing process is fully automatic, which makes it versatile, lightweight and easy-to-deploy. Our approach tracks accurate sparse 2D features between automatically selected key frames to animate a parametric blend shape model, which is further refined in pose, expression and shape by temporally coherent optical flow and photometric stereo. We demonstrate performance capture results for long and complex face sequences captured indoors and outdoors, and we exemplify the relevance of our approach as an enabling technology for model-based face editing in movies and video, such as adding new facial textures, as well as a step towards enabling everyone to do facial performance capture with a single affordable camera.
@cite_39: We present a novel algorithm aiming to estimate the 3D shape, the texture of a human face, along with the 3D pose and the light direction from a single photograph by recovering the parameters of a 3D morphable model. Generally, the algorithms tackling the problem of 3D shape estimation from image data use only the pixels intensity as input to drive the estimation process. This was previously achieved using either a simple model, such as the Lambertian reflectance model, leading to a linear fitting algorithm. Alternatively, this problem was addressed using a more precise model and minimizing a non-convex cost function with many local minima. One way to reduce the local minima problem is to use a stochastic optimization algorithm. However, the convergence properties (such as the radius of convergence) of such algorithms, are limited. Here, as well as the pixel intensity, we use various image features such as the edges or the location of the specular highlights. The 3D shape, texture and imaging parameters are then estimated by maximizing the posterior of the parameters given these image features. The overall cost function obtained is smoother and, hence, a stochastic optimization algorithm is not needed to avoid the local minima problem. This leads to the multi-features fitting algorithm that has a wider radius of convergence and a higher level of precision. This is shown on some example photographs, and on a recognition experiment performed on the CMU-PIE image database.
@cite_23: 
@cite_11: 
Conventional methods for single-view 3DMM fitting are mostly based on analysis-by-synthesis optimization @cite_37 @cite_39 @cite_1 @cite_8 @cite_23 @cite_11 , by constraining the data similarities like pixel colors, facial landmarks, edges, , between observed images and the synthetic images induced by 3DMM. The optimization is usually sensitive to initial conditions and parameters, and hence brittle in practice. This leads to the recent interests in regression-based approaches with deep neural networks.