Facial Editing Research Articles

Face super resolution with a high frequency highway

AbstractFace shape priors such as landmarks, heatmaps, and parsing maps are widely used to improve face super resolution (SR). It is observed that face priors provide locations of high‐frequency details in key facial areas such as the eyes and mouth. However, existing methods fail to effectively exploit the high‐frequency information by using the priors as either constraints or inputs. This paper proposes a novel high frequency highway () framework to better utilize prior information for face SR, which dynamically decomposes the final SR face into a coarse SR face and a high frequency (HF) face. The coarse SR face is reconstructed from a low‐resolution face via a texture branch, using only pixel‐wise reconstruction loss. Meanwhile, the HF face is directly generated from face priors via an HF branch that employs the proposed inception–hourglass model. As a result, allows the face priors to have a direct impact on the SR face by adding the outputs of both branches as the final result and provides an extra face editing function. Extensive experiments show that significantly outperforms state‐of‐the‐art face SR methods, is general for different texture branch models and face priors, and is robust to dataset mismatch and pose variations.

Talk-to-Edit: Fine-Grained 2D and 3D Facial Editing via Dialog.

Facial editing is to manipulate the facial attributes of a given face image. Nowadays, with the development of generative models, users can easily generate 2D and 3D facial images with high fidelity and 3D-aware consistency. However, existing works are incapable of delivering a continuous and fine-grained editing mode (e.g., editing a slightly smiling face to a big laughing one) with natural interactions with users. In this work, we propose Talk-to-Edit, an interactive facial editing framework that performs fine-grained attribute manipulation through dialog between the user and the system. Our key insight is to model a continual "semantic field" in the GAN latent space. 1) Unlike previous works that regard the editing as traversing straight lines in the latent space, here the fine-grained editing is formulated as finding a curving trajectory that respects fine-grained attribute landscape on the semantic field. 2) The curvature at each step is location-specific and determined by the input image as well as the users' language requests. 3) To engage the users in a meaningful dialog, our system generates language feedback by considering both the user request and the current state of the semantic field. We demonstrate the effectiveness of our proposed framework on both 2D and 3D-aware generative models. We term the semantic field for the 3D-aware models as "tri-plane" flow, as it corresponds to the changes not only in the color space but also in the density space. We also contribute CelebA-Dialog, a visual-language facial editing dataset to facilitate large-scale study. Specifically, each image has manually annotated fine-grained attribute annotations as well as template-based textual descriptions in natural language. Extensive quantitative and qualitative experiments demonstrate the superiority of our framework in terms of 1) the smoothness of fine-grained editing, 2) the identity/attribute preservation, and 3) the visual photorealism and dialog fluency. Notably, the user study validates that our overall system is consistently favored by around 80% of the participants.

Key‐point‐guided adaptive convolution and instance normalization for continuous transitive face reenactment of any person

AbstractFace reenactment technology is widely applied in various applications. However, the reconstruction effects of existing methods are often not quite realistic enough. Thus, this paper proposes a progressive face reenactment method. First, to make full use of the key information, we propose adaptive convolution and instance normalization to encode the key information into all learnable parameters in the network, including the weights of the convolution kernels and the means and variances in the normalization layer. Second, we present continuous transitive facial expression generation according to all the weights of the network generated by the key points, resulting in the continuous change of the image generated by the network. Third, in contrast to classical convolution, we apply the combination of depth‐ and point‐wise convolutions, which can greatly reduce the number of weights and improve the efficiency of training. Finally, we extend the proposed face reenactment method to the face editing application. Comprehensive experiments demonstrate the effectiveness of the proposed method, which can generate a clearer and more realistic face from any person and is more generic and applicable than other methods.

StyleAdv: A Usable Privacy Framework Against Facial Recognition with Adversarial Image Editing

In this era of ubiquitous surveillance and online presence, protecting facial privacy has become a critical concern for individuals and society as a whole. Adversarial attacks have emerged as a promising solution to this problem, but current methods are limited in quality or are impractical for sensitive domains such as facial editing. This paper presents a novel adversarial image editing framework called StyleAdv, which leverages StyleGAN's latent spaces to generate powerful adversarial images, providing an effective tool against facial recognition systems. StyleAdv achieves high success rates by employing meaningful facial editing with StyleGAN while maintaining image quality, addressing a challenge faced by existing methods. To do so, the comprehensive framework integrates semantic editing, adversarial attacks, and face recognition systems, providing a cohesive and robust tool for privacy protection. We also introduce the ``residual attack`` strategy, using residual information to enhance attack success rates. Our evaluation offers insights into effective editing, discussing tradeoffs in latent spaces, optimal edits for our optimizer, and the impact of utilizing residual information. Our approach is transferable to state-of-the-art facial recognition systems, making it a versatile tool for privacy protection. In addition, we provide a user-friendly interface with multiple editing options to help users create effective adversarial images. Extensive experiments are used to provide insights and demonstrate that StyleAdv outperforms state-of-the-art methods in terms of both attack success rate and image quality. By providing a versatile tool for generating high-quality adversarial samples, StyleAdv can be used both to enhance individual users' privacy and to stimulate advances in adversarial attack and defense research.

Learning physically based material and lighting decompositions for face editing

Lighting is crucial for portrait photography, yet the complex interactions between the skin and incident light are expensive to model computationally in graphics and difficult to reconstruct analytically via computer vision. Alternatively, to allow fast and controllable reflectance and lighting editing, we developed a physically based decomposition through deep learned priors from path-traced portrait images. Previous approaches that used simplified material models or low-frequency or low-dynamic-range lighting struggled to model specular reflections or relight directly without intermediate decomposition. However, we estimate the surface normal, skin albedo and roughness, and high-frequency HDRI maps, and propose an architecture to estimate both diffuse and specular reflectance components. In our experiments, we show that this approach can represent the true appearance function more effectively than simpler baseline methods, leading to better generalization and higher-quality editing.

FACEMUG: A Multimodal Generative and Fusion Framework for Local Facial Editing.

Existing facial editing methods have achieved remarkable results, yet they often fall short in supporting multimodal conditional local facial editing. One of the significant evidences is that their output image quality degrades dramatically after several iterations of incremental editing, as they do not support local editing. In this paper, we present a novel multimodal generative and fusion framework for globally-consistent local facial editing (FACEMUG) that can handle a wide range of input modalities and enable fine-grained and semantic manipulation while remaining unedited parts unchanged. Different modalities, including sketches, semantic maps, color maps, exemplar images, text, and attribute labels, are adept at conveying diverse conditioning details, and their combined synergy can provide more explicit guidance for the editing process. We thus integrate all modalities into a unified generative latent space to enable multimodal local facial edits. Specifically, a novel multimodal feature fusion mechanism is proposed by utilizing multimodal aggregation and style fusion blocks to fuse facial priors and multimodalities in both latent and feature spaces. We further introduce a novel self-supervised latent warping algorithm to rectify misaligned facial features, efficiently transferring the pose of the edited image to the given latent codes. We evaluate our FACEMUG through extensive experiments and comparisons to state-of-the-art (SOTA) methods. The results demonstrate the superiority of FACEMUG in terms of editing quality, flexibility, and semantic control, making it a promising solution for a wide range of local facial editing tasks.

MaGAT: Mask-Guided Adversarial Training for Defending Face Editing GAN Models From Proactive Defense

MaGAT: Mask-Guided Adversarial Training for Defending Face Editing GAN Models From Proactive Defense

A Chinese Face Dataset with Dynamic Expressions and Diverse Ages Synthesized by Deep Learning

Facial stimuli have gained increasing popularity in research. However, the existing Chinese facial datasets primarily consist of static facial expressions and lack variations in terms of facial aging. Additionally, these datasets are limited to stimuli from a small number of individuals, in that it is difficult and time-consuming to recruit a diverse range of volunteers across different age groups to capture their facial expressions. In this paper, a deep-learning based face editing approach, StyleGAN, is used to synthesize a Chinese face dataset, namely SZU-EmoDage, where faces with different expressions and ages are synthesized. Leverage on the interpolations of latent vectors, continuously dynamic expressions with different intensities, are also available. Participants assessed emotional categories and dimensions (valence, arousal and dominance) of the synthesized faces. The results show that the face database has good reliability and validity, and can be used in relevant psychological experiments. The availability of SZU-EmoDage opens up avenues for further research in psychology and related fields, allowing for a deeper understanding of facial perception.

Image-to-Image Translation with Disentangled Latent Vectors for Face Editing.

We propose an image-to-image translation framework for facial attribute editing with disentangled interpretable latent directions. Facial attribute editing task faces the challenges of targeted attribute editing with controllable strength and disentanglement in the representations of attributes to preserve the other attributes during edits. For this goal, inspired by the latent space factorization works of fixed pretrained GANs, we design the attribute editing by latent space factorization, and for each attribute, we learn a linear direction that is orthogonal to the others. We train these directions with orthogonality constraints and disentanglement losses. To project images to semantically organized latent spaces, we set an encoder-decoder architecture with attention-based skip connections. We extensively compare with previous image translation algorithms and editing with pretrained GAN works. Our extensive experiments show that our method significantly improves over the state-of-the-arts. Project page: https://yusufdalva.github.io/vecgan.

Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in VIS and NIR Scenario

In recent years, with the rapid development of face editing and generation, more and more fake videos are circulating on social media, which has caused extreme public concerns. Existing face forgery detection methods based on frequency domain find that the GAN forged images have obvious grid-like visual artifacts in the frequency spectrum. But for synthesized videos, these methods only confine to a single frame and pay little attention to the most discriminative part and temporal frequency clue among different frames. To take full advantage of the rich information in video sequences, this paper performs video forgery detection on both spatial and temporal frequency domains and proposes a Discrete Cosine Transform-based Forgery Clue Augmentation Network (FCAN-DCT) to achieve a more comprehensive spectrum spatial-temporal feature representation. FCAN-DCT totally consists of a backbone network and two branches: Compact Feature Extraction (CFE) module and Frequency Temporal Attention (FTA) module. We conduct thorough experimental assessments on three visible light (VIS) based datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., FaceForensics++, Celeb-DF (v2), Wild-Deepfake), and our self-built video forgery dataset DeepfakeNIR, which is the first video forgery dataset on near-infrared (NIR) modality. The experimental results demonstrate the effectiveness and robustness of our method for detecting forgery videos in both VIS and NIR scenarios.DeepfakeNIR and code are available at https://github.com/AEP-WYK/DeepfakeNIR.

"My face, my rules": Enabling Personalized Protection Against Unacceptable Face Editing

Today, face editing is widely used to refine/alter photos in both professional and recreational settings. Yet it is also used to modify (and repost) existing online photos for cyberbullying. Our work considers an important open question: 'How can we support the collaborative use of face editing on social platforms while protecting against unacceptable edits and reposts by others?' This is challenging because, as our user study shows, users vary widely in their definition of what edits are (un)acceptable. Any global filter policy deployed by social platforms is unlikely to address the needs of all users, but hinders social interactions enabled by photo editing. Instead, we argue that face edit protection policies should be implemented by social platforms based on individual user preferences. When posting an original photo online, a user can choose to specify the types of face edits (dis)allowed on the photo. Social platforms use these per-photo edit policies to moderate future photo uploads, i.e., edited photos containing modifications that violate the original photo's policy are either blocked or shelved for user approval. Realizing this personalized protection, however, faces two immediate challenges: (1) how to accurately recognize specific modifications, if any, contained in a photo; and (2) how to associate an edited photo with its original photo (and thus the edit policy). We show that these challenges can be addressed by combining highly efficient hashing based image search and scalable semantic image comparison, and build a prototype protector (Alethia) covering nine edit types. Evaluations using IRB-approved user studies and data-driven experiments (on 839K face photos) show that Alethia accurately recognizes edited photos that violate user policies and induces a feeling of protection to study participants. This demonstrates the initial feasibility of personalized face edit protection. We also discuss current limitations and future directions to push the concept forward.

Aggregated Contextual Transformations for High-Resolution Image Inpainting.

Image inpainting that completes large free-form missing regions in images is a promising yet challenging task. State-of-the-art approaches have achieved significant progress by taking advantage of generative adversarial networks (GAN). However, these approaches can suffer from generating distorted structures and blurry textures in high-resolution images (e.g., 512×512). The challenges mainly drive from (1) image content reasoning from distant contexts, and (2) fine-grained texture synthesis for a large missing region. To overcome these two challenges, we propose an enhanced GAN-based model, named Aggregated COntextual-Transformation GAN (AOT-GAN), for high-resolution image inpainting. Specifically, to enhance context reasoning, we construct the generator of AOT-GAN by stacking multiple layers of a proposed AOT block. The AOT blocks aggregate contextual transformations from various receptive fields, allowing to capture both informative distant image contexts and rich patterns of interest for context reasoning. For improving texture synthesis, we enhance the discriminator of AOT-GAN by training it with a tailored mask-prediction task. Such a training objective forces the discriminator to distinguish the detailed appearances of real and synthesized patches, and in turn facilitates the generator to synthesize clear textures. Extensive comparisons on Places2, the most challenging benchmark with 1.8 million high-resolution images of 365 complex scenes, show that our model outperforms the state-of-the-art. A user study including more than 30 subjects further validates the superiority of AOT-GAN. We further evaluate the proposed AOT-GAN in practical applications, e.g., logo removal, face editing, and object removal. Results show that our model achieves promising completions in the real world. We release codes and models in https://github.com/researchmm/AOT-GAN-for-Inpainting.

Face Editing Based on Facial Recognition Features

Face editing generates a face image with the target attributes without changing the identity or other information. Current methods have achieved considerable performance; however, they cannot effectively retain the face&#x2019;s identity and semantic information while controlling the attribute intensity. Inspired by two human cognitive characteristics, namely, the principle of global precedence and the principle of homology continuity, we propose a novel face editing approach called the information retention and intensity control generative adversarial network (IricGAN). It includes a learnable hierarchical feature combination (HFC) function, which can construct a sample&#x2019;s source space through multiscale feature mixing; it can guarantee the integrity of the source space while significantly compressing the network. Additionally, the attribute regression module (ARM) can decouple different attribute paradigms in the source space to ensure the correct modification of the required attributes and preserve the other areas. The gradual process of modifying the face attributes can be simulated by applying different control strengths in the source space. In face editing experiments, both qualitative and quantitative results demonstrate that IricGAN achieves the best overall results among state-of-the-art alternatives. Target attributes can be continuously modified by re-feeding the relationship of the source space and the image, and the independence of each attribute can be retained to the greatest extent. IricGAN:https://github.com/nanfangzhe/IricGAN

Face Editing Using Part‐Based Optimization of the Latent Space

AbstractWe propose an approach for interactive 3D face editing based on deep generative models. Most of the current face modeling methods rely on linear methods and cannot express complex and non‐linear deformations. In contrast to 3D morphable face models based on Principal Component Analysis (PCA), we introduce a novel architecture based on variational autoencoders. Our architecture has multiple encoders (one for each part of the face, such as the nose and mouth) which feed a single decoder. As a result, each sub‐vector of the latent vector represents one part. We train our model with a novel loss function that further disentangles the space based on different parts of the face. The output of the network is a whole 3D face. Hence, unlike part‐based PCA methods, our model learns to merge the parts intrinsically and does not require an additional merging process. To achieve interactive face modeling, we optimize for the latent variables given vertex positional constraints provided by a user. To avoid unwanted global changes elsewhere on the face, we only optimize the subset of the latent vector that corresponds to the part of the face being modified. Our editing optimization converges in less than a second. Our results show that the proposed approach supports a broader range of editing constraints and generates more realistic 3D faces.

Deepfakes Generation and Detection: A Short Survey

Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. DeepFakes refer to face multimedia content, which has been digitally altered or synthetically created using deep neural networks. The paper first outlines the readily available face editing apps and the vulnerability (or performance degradation) of face recognition systems under various face manipulations. Next, this survey presents an overview of the techniques and works that have been carried out in recent years for deepfake and face manipulations. Especially, four kinds of deepfake or face manipulations are reviewed, i.e., identity swap, face reenactment, attribute manipulation, and entire face synthesis. For each category, deepfake or face manipulation generation methods as well as those manipulation detection methods are detailed. Despite significant progress based on traditional and advanced computer vision, artificial intelligence, and physics, there is still a huge arms race surging up between attackers/offenders/adversaries (i.e., DeepFake generation methods) and defenders (i.e., DeepFake detection methods). Thus, open challenges and potential research directions are also discussed. This paper is expected to aid the readers in comprehending deepfake generation and detection mechanisms, together with open issues and future directions.

MaskFaceGAN: High Resolution Face Editing with Masked GAN Latent Code Optimization.

Face editing represents a popular research topic within the computer vision and image processing communities. While significant progress has been made recently in this area, existing solutions: (i) are still largely focused on low-resolution images, (ii) often generate editing results with visual artefacts, or (iii) lack fine-grained control over the editing procedure and alter multiple (entangled) attributes simultaneously, when trying to generate the desired facial semantics. In this paper, we aim to address these issues through a novel editing approach, called MaskFaceGAN that focuses on local attribute editing. The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i.e., StyleGAN2) with respect to several constraints that ensure: (i) preservation of relevant image content, (ii) generation of the targeted facial attributes, and (iii) spatially-selective treatment of local image regions. The constraints are enforced with the help of an (differentiable) attribute classifier and face parser that provide the necessary reference information for the optimization procedure. MaskFaceGAN is evaluated in extensive experiments on the FRGC, SiblingsDB-HQf, and XM2VTS datasets and in comparison with several state-of-the-art techniques from the literature. Our experimental results show that the proposed approach is able to edit face images with respect to several local facial attributes with unprecedented image quality and at high-resolutions (1024×1024), while exhibiting considerably less problems with attribute entanglement than competing solutions. The source code is publicly available from: https://github.com/MartinPernus/MaskFaceGAN.

The influence of sex and rejuvenation conversion by the face editing app on facial attractiveness -Evaluation of facial attractiveness in images of others-

The influence of sex and rejuvenation conversion by the face editing app on facial attractiveness -Evaluation of facial attractiveness in images of others-

Fine-Grained Face Editing via Personalized Spatial-Aware Affine Modulation

Fine-Grained Face Editing via Personalized Spatial-Aware Affine Modulation

TextFace: Text-to-Style Mapping Based Face Generation and Manipulation

As a sub-topic of Text-to-Image synthesis, Text-to-Face generation has a great potential in face related applications. In this paper, we propose a generic Text-to-Face framework, namely TextFace, to achieve diverse and high-quality face image generation from text description. We introduce a novel method called Text-to-Style mapping, where the text description can be directly encoded into the latent space of a pretrained StyleGAN. Guided by our text-image similarity matching and face captioning based text alignment, the textual latent code can be fed into a well-trained StyleGAN's generator, to produce diverse face images with high resolution (1024 1024). Furthermore, our model inherently supports the semantic face editing using text descriptions. Finally, experimental results quantitatively and qualitatively demonstrate the superior performance of our model.

Consistency Preservation and Feature Entropy Regularization for GAN Based Face Editing

Consistency Preservation and Feature Entropy Regularization for GAN Based Face Editing