Human faces contain rich semantic information that could hardly be described without a large vocabulary and complex sentence patterns. However, most existing text-to-image synthesis methods could only generate meaningful results based on limited sentence templates with words contained in the training set, which heavily impairs the generalization ability of these models. In this paper, we define a novel 'free-style' text-to-face generation and manipulation problem, and propose an effective solution, named AnyFace++, which is applicable to a much wider range of open-world scenarios. The CLIP model is involved in AnyFace++ for learning an aligned language-vision feature space, which also expands the range of acceptable vocabulary as it is trained on a large-scale dataset. To further improve the granularity of semantic alignment between text and images, a memory module is incorporated to convert the description with arbitrary length, format, and modality into regularized latent embeddings representing discriminative attributes of the target face. Moreover, the diversity and semantic consistency of generation results are improved by a novel semi-supervised training scheme and a series of newly proposed objective functions. Compared to state-of-the-art methods, AnyFace++ is capable of synthesizing and manipulating face images based on more flexible descriptions and producing realistic images with higher diversity.

Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to render desired attributes, which has received significant attention due to its broad practical applications ranging from digital entertainment to biometric forensics. In the last decade, with the remarkable success of Generative Adversarial Networks (GANs) in synthesizing realistic images, numerous GAN-based models have been proposed to solve FAM with various problem formulation approaches and guiding information representations. This paper presents a comprehensive survey of GAN-based FAM methods with a focus on summarizing their principal motivations and technical details. The main contents of this survey include: (i) an introduction to the research background and basic concepts related to FAM, (ii) a systematic review of GAN-based FAM methods in three main categories, and (iii) an in-depth discussion of important properties of FAM methods, open issues, and future research directions. This survey not only builds a good starting point for researchers new to this field but also serves as a reference for the vision community.

Generative Adversarial Networks with style-based generators could successfully synthesize realistic images from input latent code. Moreover, recent studies have revealed that interpretable translations of generated images could be obtained by linearly traversing in the latent space. However, in most existing latent spaces, linear interpolation often leads to ‘spatially entangled modification’ in the manipulation result, which is undesirable in many real-world applications where local editing is required. To solve this problem, we propose to manipulate the latent code in the ‘style space’ and analyze its advantage in achieving spatial disentanglement. Furthermore, we point out the weakness of simply interpolating in the style space and propose ‘Style Intervention’, a lightweight optimization-based algorithm, to further improve the visual fidelity of manipulation results. The performance of our method is verified with the task of attribute editing on high-resolution face images. Both qualitative and quantitative results demonstrate the advantage of image translation in the style space and the effectiveness of our method on both real and synthetic images.

Semantic portrait synthesis has drawn consistent attention and has made significant progress, yet achieving style diversity and semantic controllability simultaneously is still a challenge. Existing methods either 1) directly take a semantic label map as input, ignoring various possibilities of semantic styles, or 2) sample global noise as input, ignoring controllability of local semantics. To fill this gap, we propose semantic-aware noise, a simple but effective input that tackles both issues and shows improved results over baselines. Semantic-aware noise introduces semantic information into noise, and each semantic is sampled from the noise separately, combining the semantic controllability and the noise sampling diversity. To further expand and manipulate real images, we propose a novel ternary network structure, allowing simultaneous diverse semantic image synthesis and real image manipulation in a unified framework. Extensive experiments demonstrate that the proposed method achieves quantitatively superior and perceptually pleasing results compared to state-of-the-art methods. We also analyze the performance of our method with respect to different noise structures and real-life applications in diverse synthesis, interactive manipulation, and extreme pose scenarios.

Existing text-to-image synthesis methods generally are only applicable to words in the training dataset. However, human faces are so variable to be described with limited words. So this paper proposes the first free-style text-to-face method namely AnyFace enabling much wider open world applications such as metaverse, social media, cosmetics, forensics, etc. AnyFace has a novel two-stream framework for face image synthesis and manipulation given arbitrary descriptions of the human face. Specifically, one stream performs text-to-face generation and the other conducts face image reconstruction. Facial text and image features are extracted using the CLIP (Contrastive Language-Image Pre-training) encoders. And a collaborative Cross Modal Distillation (CMD) module is designed to align the linguistic and visual features across these two streams. Furthermore, a Diverse Triplet Loss (DT loss) is developed to model fine-grained features and improve facial diversity. Extensive experiments on Multi-modal CelebA-HQ and CelebAText-HQ demonstrate significant advantages of AnyFace over state-of-the-art methods. AnyFace can achieve high-quality, high-resolution, and high-diversity face synthesis and manipulation results without any constraints on the number and content of input captions.

In recent years, significant progress has been achieved in face image editing due to the success of Generative Adversarial Network (GAN). However, state-of-the-art face editing methods mainly suffer from the following two limitations: 1) they are only applicable to face images with relative low-resolutions and 2) multi-attribute face editing may generate uncontrollable changes in non-target face attribute categories. To solve these problems, we propose a novel High-Quality Generative Adversarial Network (HQ-GAN) for controllable editing of multiple face attributes in high-resolution images. HQ-GAN has two novel ideas to break the limitations of resolution and controllability correspondingly: 1) fine-grained textures and realistic details of high-resolution face images are better preserved with the aid of textural features extracted by the wavelet transform module and 2) desired multi-attribute targets of face editing are emphasized using a weighted binary cross-entropy (BCE) loss so that the influence on non-target attributes is greatly reduced. To the best of our knowledge, HQ-GAN is the first attempt to achieve continuous editing of multiple face attributes on high-resolution images of the CelebA-HQ using only 28 000 training samples. Extensive qualitative results demonstrate the superiority of the proposed method in rendering realistic high-resolution face images with accurate attribute modification, and comprehensive quantitative results show that the proposed method significantly outperforms state-of-the-art face editing methods.

Face portrait editing has achieved great progress in recent years. However, previous methods either 1) operate on pre-defined face attributes, lacking the flexibility of controlling shapes of high-level semantic facial components (e.g., eyes, nose, mouth), or 2) take manually edited mask or sketch as an intermediate representation for observable changes, but such additional input usually requires extra efforts to obtain. To break the limitations (e.g. shape, mask or sketch) of the existing methods, we propose a novel framework termed r FACE (Reference Guided FAce Component Editing) for diverse and controllable face component editing with geometric changes. Specifically, r-FACE takes an image inpainting model as the backbone, utilizing reference images as conditions for controlling the shape of face components. In order to encourage the framework to concentrate on the target face components, an example-guided attention module is designed to fuse attention features and the target face component features extracted from the reference image. Through extensive experimental validation and comparisons, we verify the effectiveness of the proposed framework.