Joint 3D facial shape reconstruction and texture completion from a single image

Recent years have witnessed significant progress in image-based 3D face reconstruction using deep convolutional neural networks. However, current reconstruction methods often perform improperly in self-occluded regions and can lead to inaccurate correspondences between a 2D input image and a 3D face template, hindering use in real applications. To address these problems, we propose a deep shape reconstruction and texture completion network, SRTC-Net, which jointly reconstructs 3D facial geometry and completes texture with correspondences from a single input face image. In SRTC-Net, we leverage the geometric cues from completed 3D texture to reconstruct detailed structures of 3D shapes. The SRTC-Net pipeline has three stages. The first introduces a correspondence network to identify pixel-wise correspondence between the input 2D image and a 3D template model, and transfers the input 2D image to a U-V texture map. Then we complete the invisible and occluded areas in the U-V texture map using an inpainting network. To get the 3D facial geometries, we predict coarse shape (U-V position maps) from the segmented face from the correspondence network using a shape network, and then refine the 3D coarse shape by regressing the U-V displacement map from the completed U-V texture map in a pixel-to-pixel way. We examine our methods on 3D reconstruction tasks as well as face frontalization and pose invariant face recognition tasks, using both in-the-lab datasets (MICC, MultiPIE) and in-the-wild datasets (CFP). The qualitative and quantitative results demonstrate the effectiveness of our methods on inferring 3D facial geometry and complete texture; they outperform or are comparable to the state-of-the-art.

Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior

Over the past few decades, numerous attempts have been made to address the problem of recovering a high-resolution (HR) facial image from its corresponding low-resolution (LR) counterpart, a task commonly referred to as face hallucination. Despite the impressive performance achieved by position-patch and deep learning-based methods, most of these techniques are still unable to recover identity-specific features of faces. The former group of algorithms often produces blurry and oversmoothed outputs particularly in the presence of higher levels of degradation, whereas the latter generates faces which sometimes by no means resemble the individuals in the input images. In this paper, a novel face super-resolution approach will be introduced, in which the hallucinated face is forced to lie in a subspace spanned by the available training faces. Therefore, in contrast to the majority of existing face hallucination techniques and thanks to this <i>face subspace prior</i>, the reconstruction is performed in favor of recovering person-specific facial features, rather than merely increasing image quantitative scores. Furthermore, inspired by recent advances in the area of 3D face reconstruction, an efficient 3D dictionary alignment scheme is also presented, through which the algorithm becomes capable of dealing with low-resolution faces taken in uncontrolled conditions. In extensive experiments carried out on several well-known face datasets, the proposed algorithm shows remarkable performance by generating detailed and close to ground truth results which outperform the state-of-the-art face hallucination algorithms by significant margins both in quantitative and qualitative evaluations.

Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior

Over the past few decades, numerous attempts have been made to address the problem of recovering a high-resolution (HR) facial image from its corresponding low-resolution (LR) counterpart, a task commonly referred to as face hallucination. Despite the impressive performance achieved by position-patch and deep learning-based methods, most of these techniques are still unable to recover identity-specific features of faces. The former group of algorithms often produces blurry and oversmoothed outputs particularly in the presence of higher levels of degradation, whereas the latter generates faces which sometimes by no means resemble the individuals in the input images. In this paper, a novel face super-resolution approach will be introduced, in which the hallucinated face is forced to lie in a subspace spanned by the available training faces. Therefore, in contrast to the majority of existing face hallucination techniques and thanks to this <i>face subspace prior</i>, the reconstruction is performed in favor of recovering person-specific facial features, rather than merely increasing image quantitative scores. Furthermore, inspired by recent advances in the area of 3D face reconstruction, an efficient 3D dictionary alignment scheme is also presented, through which the algorithm becomes capable of dealing with low-resolution faces taken in uncontrolled conditions. In extensive experiments carried out on several well-known face datasets, the proposed algorithm shows remarkable performance by generating detailed and close to ground truth results which outperform the state-of-the-art face hallucination algorithms by significant margins both in quantitative and qualitative evaluations.

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.

Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement

We introduce a highly robust GAN-based framework for digitizing a normalized 3D avatar of a person from a single unconstrained photo. While the input image can be of a smiling person or taken in extreme lighting conditions, our method can reliably produce a high-quality textured model of a person's face in neutral expression and skin textures under diffuse lighting condition. Cutting-edge 3D face reconstruction methods use non-linear morphable face models combined with GAN-based decoders to capture the likeness and details of a person but fail to produce neutral head models with unshaded albedo textures which is critical for creating relightable and animation-friendly avatars for integration in virtual environments. The key challenges for existing methods to work is the lack of training and ground truth data containing normalized 3D faces. We propose a two-stage approach to address this problem. First, we adopt a highly robust normalized 3D face generator by embedding a non-linear morphable face model into a StyleGAN2 network. This allows us to generate detailed but normalized facial assets. This inference is then followed by a perceptual refinement step that uses the generated assets as regularization to cope with the limited available training samples of normalized faces. We further introduce a Normalized Face Dataset, which consists of a combination photogrammetry scans, carefully selected photographs, and generated fake people with neutral expressions in diffuse lighting conditions. While our prepared dataset contains two orders of magnitude less subjects than cutting edge GAN-based 3D facial reconstruction methods, we show that it is possible to produce high-quality normalized face models for very challenging unconstrained input images, and demonstrate superior performance to the current state-of-the-art.