Depth-Based 3D Face Reconstruction and Pose Estimation Using Shape-Preserving Domain Adaptation

Foetal alcohol syndrome (FAS) is a preventable condition caused by maternal alcohol consumption during pregnancy. The FAS facial phenotype is an important factor for diagnosis, alongside central nervous system impairments and growth abnormalities. Current methods for analysing the FAS facial phenotype rely on 3D facial image data, obtained from costly and complex surface scanning devices. An alternative is to use 2D images, which are easy to acquire with a digital camera or smart phone. However, 2D images lack the geometric accuracy required for accurate facial shape analysis. Our research offers a solution through the reconstruction of 3D human faces from single or multiple 2D images. We have developed a framework for evaluating 3D human face reconstruction from a single-input 2D image using a 3D face model for potential use in FAS assessment. We first built a generative morphable model of the face from a database of registered 3D face scans with diverse skin tones. Then we applied this model to reconstruct 3D face surfaces from single frontal images using a model-driven sampling algorithm. The accuracy of the predicted 3D face shapes was evaluated in terms of surface reconstruction error and the accuracy of FAS-relevant landmark locations and distances. Results show an average root mean square error of 2.62 mm. Our framework has the potential to estimate 3D landmark positions for parts of the face associated with the FAS facial phenotype. Future work aims to improve on the accuracy and adapt the approach for use in clinical settings. Significance: Our study presents a framework for constructing and evaluating a 3D face model from 2D face scans and evaluating the accuracy of 3D face shape predictions from single images. The results indicate low generalisation error and comparability to other studies. The reconstructions also provide insight into specific regions of the face relevant to FAS diagnosis. The proposed approach presents a potential cost-effective and easily accessible imaging tool for FAS screening, yet its clinical application needs further research.

In this paper, a novel framework for 3D face reconstruction from a single 2D face image was proposed. We focus on generating 3D face model without expensive devices and complicated calculation. First, we preprocess 2D face image, including illumination compensation, face detection and feature point extraction. Then, the method is based on a 3D morphable face model that encodes shape and texture in terms of model parameters. The prior 3D face model is a linear combination of “eigenheads” obtained by applying PCA to a training set of laser-scanned 3D faces. To account for pose and illumination variations, the algorithm simulates the process of image formation in 3D face and it estimates 3D face and texture of faces from a single image. As opposed to previous literature, our method can locate facial feature points automatically in a 2D facial image. Moreover, we also show that our proposed method for pose estimation can be successfully applied to 3D face reconstruction. In our experiment results, the method proposed has a satisfied performance regarding calculating time and the reconstructing photorealistic 3D faces.

3D face reconstruction from a single-view image in the wild is a long-standing challenging problem. Traditional 3DMM-based methods directly regressed parameters, which probably caused that the network learned the discriminative informative features in the face insufficiently. In this paper, we propose a replay attention and data augmentation network (RADAN) for 3D dense alignment and face reconstruction. Instead of the traditional attention mechanism, our replay attention module aims to increase the sensitivity of the network to informative features by adaptively recalibrating the weight response in the attention mechanism, which typically reinforces the distinguishability of the learned feature representation. In this way, the network is able to further improve the accuracy of face reconstruction and dense alignment in an unconstrained environment. Moreover, to improve the generalization performance of the model and the ability of the network to capture local details, we present a data augmentation strategy to preprocess the sample data, which generates the images that contain more local details and occluded face in a cropping and pasting manner. Extensive qualitative and quantitative experimental results on widely-evaluated benchmarking datasets demonstrate that our approach achieves competitive performance compared to state-of-the-art methods. Code is available at https://github.com/zhouzhiyuanl/RADANet.

Example‐based statistical face models using principle component analysis (PCA) have been widely deployed for three‐dimensional (3D) face reconstruction and face recognition. The two common factors that are generally concerned with such models are the size of the training dataset and the selection of different examples in the training set. The representational power (RP) of an example‐based model is its capability to depict a new 3D face for a given 2D face image. The RP of the model can be increased by correspondingly increasing the number of training samples. In this contribution, a novel approach is proposed to increase the RP of the 3D face reconstruction model by deforming a set of examples in the training dataset. A PCA‐based 3D face model is adapted for each new near frontal input face image to reconstruct the 3D face shape. Further an extended Tikhonov regularisation method has been employed to reconstruct 3D face shapes from a set of facial points. The results justify that the proposed adaptive PCA‐based model considerably improves the RP of the standard PCA‐based model and outperforms it with a 95% confidence level.

We tackle the practical problem of hand pose estimation from a single noisy depth image. A dedicated three-step pipeline is proposed: Initial estimation step provides an initial estimation of the hand in-plane orientation and 3D location, Candidate generation step produces a set of 3D pose candidate from the Hough voting space with the help of the rotational invariant depth features, Verification step delivers the final 3D hand pose as the solution to an optimization problem. We analyze the depth noises, and suggest tips to minimize their negative impacts on the overall performance. Our approach is able to work with Kinect-type noisy depth images, and reliably produces pose estimations of general motions efficiently (12 frames per second). Extensive experiments are conducted to qualitatively and quantitatively evaluate the performance with respect to the state-of-the-art methods that have access to additional RGB images. Our approach is shown to deliver on par or even better results.

T3D face reconstruction from a single 2D image is mathematically ill-posed. However, to solve ill-posed problems in the area of computer vision, a variety of methods has been proposed; some of the solutions are to estimate latent information or to apply model based approaches. In this paper, we propose a novel method to reconstruct a 3D face from a single 2D face image based on pose estimation and a deformable model of 3D face shape. For 3D face reconstruction from a single 2D face image, it is the first task to estimate the depth lost by 2D projection of 3D faces. Applying the EM algorithm to facial landmarks in a 2D image, we propose a pose estimation algorithm to infer the pose parameters of rotation, scaling, and translation. After estimating the pose, much denser points are interpolated between the landmark points by a 3D deformable model and barycentric coordinates. As opposed to previous literature, our method can locate facial feature points automatically in a 2D facial image. Moreover, we also show that the proposed method for pose estimation can be successfully applied to 3D face reconstruction. Experiments demonstrate that our approach can produce reliable results for reconstructing photorealistic 3D faces.

3D human pose estimation aims to reconstruct the human skeleton of all the individuals in a scene by detecting several body joints. The creation of accurate and efficient methods is required for several real-world applications including animation, human–robot interaction, surveillance systems or sports, among many others. However, several obstacles such as occlusions, random camera perspectives, or the scarcity of 3D labelled data, have been hampering the models’ performance and limiting their deployment in real-world scenarios. The higher availability of cameras has led researchers to explore multi-view solutions due to the advantage of being able to exploit different perspectives to reconstruct the pose. Most existing reviews focus mainly on monocular 3D human pose estimation and a comprehensive survey only on multi-view approaches to determine the 3D pose has been missing since 2012. Thus, the goal of this survey is to fill that gap and present an overview of the methodologies related to 3D pose estimation in multi-view settings, understand what were the strategies found to address the various challenges and also, identify their limitations. According to the reviewed articles, it was possible to find that most methods are fully-supervised approaches based on geometric constraints. Nonetheless, most of the methods suffer from 2D pose mismatches, to which the incorporation of temporal consistency and depth information have been suggested to reduce the impact of this limitation, besides working directly with 3D features can completely surpass this problem but at the expense of higher computational complexity. Models with lower supervision levels were identified to overcome some of the issues related to 3D pose, particularly the scarcity of labelled datasets. Therefore, no method is yet capable of solving all the challenges associated with the reconstruction of the 3D pose. Due to the existing trade-off between complexity and performance, the best method depends on the application scenario. Therefore, further research is still required to develop an approach capable of quickly inferring a highly accurate 3D pose with bearable computation cost. To this goal, techniques such as active learning, methods that learn with a low level of supervision, the incorporation of temporal consistency, view selection, estimation of depth information and multi-modal approaches might be interesting strategies to keep in mind when developing a new methodology to solve this task. • First review only on multi-view, multi-modal methods to estimate 3D pose since 2012. • Multi-view allows capturing the full body geometry, making 3D pose estimation easier. • Real-world applications include sports, broadcasting, rehabilitation or animation. • Finding a fast, accurate method with low computational cost remains a challenge. • Multi-modal methods or view selection can lead to an efficient and effective model.

2D face images represent faces with incomplete information. 3D face reconstruction from a single 2D image is a challenging problem with application value. The single feature extraction method distorts the generated 3D face image. In this paper, we use contour-based face segmentation method to reconstruct 3D face image. We focus on the edge and contour information of the face when using contour lines to segment the face. Different from the global 3D face reconstruction method, we combine the global and local face information to carry out 3D face reconstruction. Our method: First of all, we do contour segmentation for human faces and extract the features of the segmented images. Second, we learn the local binary features of each keypoint in a complete face image, then combine the features and use linear regression to detect the keypoints. Thirdly, we use Convolutional Neural Networks to learn the regression 3D Morphable Model coefficient and significantly improve the quality and efficiency of reconstruction. We regressed the coefficients of the 3D deformable model from 2D images to present face alignment for 3D face reconstruction. We carry out feature mapping between 2D face and 3D face image, and monitor and verify 3D face model through mapping relationship. Our method can not only reconstruct face images from all angles, but also reduce face deformities. We made face images fit better under different expressions and postures.

3D face reconstruction from a single image is an important task in many multimedia applications. Recent works typically learn a CNN-based 3D face model that regresses coefficients of a 3D Morphable Model (3DMM) from 2D images to perform 3D face reconstruction. However, the shortage of training data with 3D annotations considerably limits performance of these methods. To alleviate this issue, we propose a novel 2D-Assisted Learning (2DAL) method that can effectively use “in the wild” 2D face images with noisy landmark information to substantially improve 3D face model learning. Specifically, taking the sparse 2D facial landmark heatmaps as additional information, 2DAL introduces four novel self-supervision schemes that view the 2D landmark and 3D landmark prediction as a self-mapping process, including the landmark self-prediction consistency for 2D and 3D faces respectively, cycle-consistency over the 2D landmark prediction and self-critic over the predicted 3DMM coefficients based on landmark prediction. Using these four self-supervision schemes, 2DAL significantly relieves the demands for the the conventional paired 2D-to-3D annotations and gives much higher-quality 3D face models without requiring any additional 3D annotations. Experiments on AFLW2000-3D, AFLW-LFPA and Florence benchmarks show that our method outperforms state-of-the-arts for both 3D face reconstruction and dense face alignment by a large margin.

3D face reconstruction from a single image is a classical and challenging problem, with wide applications in many areas. Inspired by recent works in face animation from RGBD or monocular video inputs, we develop a novel method for reconstructing 3D faces from unconstrained 2D images, using a coarse-to-fine optimization strategy. First, a smooth coarse 3D face is generated from an example-based bilinear face model, by aligning the projection of 3D face landmarks with 2D landmarks detected from the input image. Afterwards, using local corrective deformation fields, the coarse 3D face is refined using photometric consistency constraints, resulting in a medium face shape. Finally, a shape-from-shading method is applied on the medium face to recover fine geometric details. Our method outperforms stateof- the-art approaches in terms of accuracy and detail recovery, which is demonstrated in extensive experiments using real world models and publicly available datasets.

Learning 3D faces from 2D images via Stacked Contractive Autoencoder

Unlike 2D face images, obtaining a 3D face is not easy. Existing methods, therefore, create a 3D face from a 2D face image (3D face reconstruction). A user might wish to edit the reconstructed 3D face, but 3D face editing has seldom been studied. This paper presents such method and shows that reconstruction and editing can help each other. In the presented framework named NEO-3DF, the 3D face model we propose has independent sub-models corresponding to semantic face parts. It allows us to achieve both local intuitive editing and better 3D-to-2D alignment. Each face part in our model has a set of controllers designed to allow users to edit the corresponding features (e.g., nose height). In addition, we propose a differentiable module for blending the face parts and making it possible to automatically adjust the face parts (both the shapes and the locations) so that they are better aligned with the original 2D image. Experiments show that the results of NEO-3DF outperform existing methods in intuitive face editing and have better 3D-to-2D alignment accuracy (14% higher IoU) than global face model-based reconstruction. Code available at https://github.com/ubc-3d-vision-lab/NEO-3DF .

Weakly-supervised pre-training for 3D human pose estimation via perspective knowledge

Existing 3D face alignment and face reconstruction methods mainly focus on the accuracy of the model. When the existing methods are applied to dynamic videos, the stability and accuracy are significantly reduced. To overcome this problem, we propose a novel regression framework that strikes a balance between accuracy and stability. First, on the basis of lightweight backbone, encoder-decoder structure is used to jointly learn expression details and detailed 3D face from video images to recover shape details and their relationship to facial expression, and dynamic regression of a small number of 3D face parameters, effectively improve the speed and accuracy. Secondly, in order to further improve the stability of face landmarks in video, a jitter loss function of multi-frame image joint learning is proposed to strengthen the correlation between frames and face landmarks in video, and reduce the difference amplitude of face landmarks between adjacent frames to reduce the jitter of face landmarks. Experiments on several challenging datasets verify the effectiveness of our method.

Recent face recognition algorithm can achieve high accuracy when the tested face samples are frontal. However, when the face pose changes largely, the performance of existing methods drop drastically. Efforts on pose-robust face recognition are highly desirable, especially when each face class has only one frontal training sample. In this study, we propose a 2D face fitting-assisted 3D face reconstruction algorithm that aims at recognizing faces of different poses when each face class has only one frontal training sample. For each frontal training sample, a 3D face is reconstructed by optimizing the parameters of 3D morphable model (3DMM). By rotating the reconstructed 3D face to different views, pose virtual face images are generated to enlarge the training set of face recognition. Different from the conventional 3D face reconstruction methods, the proposed algorithm utilizes automatic 2D face fitting to assist 3D face reconstruction. We automatically locate 88 sparse points of the frontal face by 2D face-fitting algorithm. Such 2D face-fitting algorithm is so-called Random Forest Embedded Active Shape Model, which embeds random forest learning into the framework of Active Shape Model. Results of 2D face fitting are added to the 3D face reconstruction objective function as shape constraints. The optimization objective energy function takes not only image intensity, but also 2D fitting results into account. Shape and texture parameters of 3DMM are thus estimated by fitting the 3DMM to the 2D frontal face sample, which is a non-linear optimization problem. We experiment the proposed method on the publicly available CMUPIE database, which includes faces viewed from 11 different poses, and the results show that the proposed method is effective and the face recognition results toward pose variants are promising.