Text-driven person image generation is an emerging and challenging task in cross-modality image generation. Controllable person image generation promotes a wide range of applications such as digital human interaction and virtual try-on. However, previous methods mostly employ single-modality information as the prior condition (e.g. pose-guided person image generation), or utilize the preset words for text-driven human synthesis. Introducing a sentence composed of free words with an editable semantic pose map to describe person appearance is a more user-friendly way. In this paper, we propose HumanDiffusion, a coarse-to-fine alignment diffusion framework, for text-driven person image generation. Specifically, two collaborative modules are proposed, the Stylized Memory Retrieval (SMR) module for fine-grained feature distillation in data processing and the Multi-scale Cross-modality Alignment (MCA) module for coarse-to-fine feature alignment in diffusion. These two modules guarantee the alignment quality of the text and image, from image-level to feature-level, from low-resolution to high-resolution. As a result, HumanDiffusion realizes open-vocabulary person image generation with desired semantic poses. Extensive experiments conducted on DeepFashion demonstrate the superiority of our method compared with previous approaches. Moreover, better results could be obtained for complicated person images with various details and uncommon poses.
We present a generative model for controllable person image synthesis,as shown in Figure , which can be applied to pose-guided person image synthesis, $i.e.$, converting the pose of a source person image to the target pose while preserving the texture of that source person image, and clothing-guided person image synthesis, $i.e.$, changing the clothing texture of a source person image to the desired clothing texture. By explicitly establishing the dense correspondence between the target pose and the source image, we can effectively address the misalignment introduced by pose tranfer and genera…
Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China（脑启发智能感知与认知大学科学与技术大学实验室）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究所）
Peking University（北京大学）；Bytedance AI Lab（字节跳动人工智能实验室）
This paper introduces the Attribute-Decomposed GAN, a novel generative model for controllable person image synthesis, which can produce realistic person images with desired human attributes (e.g., pose, head, upper clothes and pants) provided in various source inputs. The core idea of the proposed model is to embed human attributes into the latent space as independent codes and thus achieve flexible and continuous control of attributes via mixing and interpolation operations in explicit style representations. Specifically, a new architecture consisting of two encoding pathways with style block…
Shanghai Key Laboratory of Multidimensional Information Processing（上海多维信息处理重点实验室）；Shanghai AI Laboratory（上海人工智能实验室）；Key Laboratory of Advanced Theory and Application in Statistics and Data Science（统计与数据科学前沿理论及应用重点实验室）；Huazhong University of Science and Technology（华中科技大学）；East China Normal University（华东师范大学）
Controllable person image synthesis task enables a wide range of applications through explicit control over body pose and appearance. In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer. The module intentionally selects the style represented by each semantic and distributes them according to the target pose. The attention matrix in cross attention expresses the dynamic similarities between the target pose and the source styles for all semantics. Therefore, it can be utilized to route the …
UVMap-ID: A Controllable and Personalized UV Map Generative Model
University of Trento（特伦托大学）；Ocean University of China（中国海洋大学）；ETH Zürich（苏黎世联邦理工学院）；PicsArt AI Research（PicsArt人工智能研究院）；Georgia Institute of Technology（佐治亚理工学院）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
本文提出 UVMap-ID，通过微调集成人脸融合模块的文本到图像扩散模型，并利用带文本与 Face ID 标注的小规模属性均衡数据集，实现可控、个性化的人物 UV Map 生成与多维质量评估。
Controllable person image generation aims to produce realistic human images with desirable attributes such as a given pose, cloth textures, or hairstyles. However, the large spatial misalignment between source and target images makes the standard image-to-image translation architectures unsuitable for this task. Most state-of-the-art methods focus on alignment for global pose-transfer tasks. However, they fail to deal with region-specific texture-transfer tasks, especially for person images with complex textures. To solve this problem, we propose a novel Spatially-Adaptive Warped Normalization…
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Tencent America（腾讯美国）；Advanced Institute of Information Technology, Peking University（北京大学高等信息科学技术研究院）
We deal with the controllable person image synthesis task which aims to re-render a human from a reference image with explicit control over body pose and appearance. Observing that person images are highly structured, we propose to generate desired images by extracting and distributing semantic entities of reference images. To achieve this goal, a neural texture extraction and distribution operation based on double attention is described. This operation first extracts semantic neural textures from reference feature maps. Then, it distributes the extracted neural textures according to the spati…
The face reenactment is a popular facial animation method where the person's identity is taken from the source image and the facial motion from the driving image. Recent works have demonstrated high quality results by combining the facial landmark based motion representations with the generative adversarial networks. These models perform best if the source and driving images depict the same person or if the facial structures are otherwise very similar. However, if the identity differs, the driving facial structures leak to the output distorting the reenactment result. We propose a novel Facial…
GAN-Avatar: Controllable Personalized GAN-based Human Head Avatar
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；University of Tübingen（图宾根大学）；Meta Reality Labs（Meta现实实验室）；Technical University of Darmstadt（达姆施塔特工业大学）
DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior
Sun Yat-sen University（中山大学）；Peng Cheng Laboratory（鹏城实验室）；ByteDance Intelligent Creation（字节跳动智能创作）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）
Controllable Localized Face Anonymization Via Diffusion Inpainting
Center for Machine Vision and Signal Analysis (CMVS)（机器视觉与信号分析中心）；University of Oulu（奥卢大学）；ELLIS Institute Finland（芬兰ELLIS研究所）；The Machine Learning Group（机器学习小组）；UiT The Arctic University of Norway（挪威北冰洋大学）
AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models
VCIP, School of Computer Science, Nankai University（VCIP，计算机学院，南开大学）；The Chinese University of Hong Kong（香港中文大学）；vivo Mobile Communication Co., Ltd（vivo移动通信有限公司）
Chinese University of Hong Kong（香港中文大学）；Texas A&M University（得克萨斯农工大学）
Flow-based generative models show great potential in image synthesis due to its reversible pipeline and exact log-likelihood target, yet it suffers from weak ability for conditional image synthesis, especially for multi-label or unaware conditions. This is because the potential distribution of image conditions is hard to measure precisely from its latent variable $z$. In this paper, based on modeling a joint probabilistic density of an image and its conditions, we propose a novel flow-based generative model named conditional adversarial generative flow (CAGlow). Instead of disentangling attrib…
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Université Bourgogne Franche-Comté（勃艮第-弗朗什-孔泰大学）
This paper proposes a new end-to-end neural rendering architecture to transfer appearance and reenact human actors. Our method leverages a carefully designed graph convolutional network (GCN) to model the human body manifold structure, jointly with differentiable rendering, to synthesize new videos of people in different contexts from where they were initially recorded. Unlike recent appearance transferring methods, our approach can reconstruct a fully controllable 3D texture-mapped model of a person, while taking into account the manifold structure from body shape and texture appearance in th…
The performance of face recognition (FR) systems applied in video surveillance has been shown to improve when the design data is augmented through synthetic face generation. This is true, for instance, with pair-wise matchers (e.g., deep Siamese networks) that typically rely on a reference gallery with one still image per individual. However, generating synthetic images in the source domain may not improve the performance during operations due to the domain shift w.r.t. the target domain. Moreover, despite the emergence of Generative Adversarial Networks (GANs) for realistic synthetic generati…
We present a method for synthesizing naturally looking images of multiple people interacting in a specific scenario. These images benefit from the advantages of synthetic data: being fully controllable and fully annotated with any type of standard or custom-defined ground truth. To reduce the synthetic-to-real domain gap, we introduce a pipeline consisting of the following steps: 1) we render scenes in a context modeled after the real world, 2) we train a human parsing model on the synthetic images, 3) we use the model to estimate segmentation maps for real images, 4) we train a conditional ge…
University of Trento（特伦托大学）；Shenzhen Graduate School, Peking University（北京大学深圳研究生院）
We propose a unified Generative Adversarial Network (GAN) for controllable image-to-image translation, i.e., transferring an image from a source to a target domain guided by controllable structures. In addition to conditioning on a reference image, we show how the model can generate images conditioned on controllable structures, e.g., class labels, object keypoints, human skeletons, and scene semantic maps. The proposed model consists of a single generator and a discriminator taking a conditional image and the target controllable structure as input. In this way, the conditional image can provi…
Baidu Research（百度研究院）；Carnegie Mellon University（卡内基梅隆大学）；City University of Hong Kong（香港城市大学）；Zhejiang University（浙江大学）；Nanyang Technological University（南洋理工大学）；Styling AI
Generation of high-quality person images is challenging, due to the sophisticated entanglements among image factors, e.g., appearance, pose, foreground, background, local details, global structures, etc. In this paper, we present a novel end-to-end framework to generate realistic person images based on given person poses and appearances. The core of our framework is a novel generator called Appearance-aware Pose Stylizer (APS) which generates human images by coupling the target pose with the conditioned person appearance progressively. The framework is highly flexible and controllable by effec…
PMMD: A pose-guided multi-view multi-modal diffusion for person generation
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；City University of Hong Kong, Hong Kong, China（香港城市大学）；Peking University, Beijing, China（北京大学）；Tsinghua University, Beijing, China（清华大学）
FaceEditTalker: Controllable Talking Head Generation with Facial Attribute Editing
Xi’an Key Laboratory of Big Data and Intelligent Vision, Xidian University, Xi’an 710071, China（西安大数据与智能视觉重点实验室，西安电子科技大学，西安710071，中国）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an 710071, China（协同智能系统重点实验室，教育部，西安电子科技大学，西安710071，中国）；School of Computer Science and Technology, Xidian University, Xi’an 710071, China（计算机科学与技术学院，西安电子科技大学，西安710071，中国）
Color3D: Controllable and Consistent 3D Colorization with Personalized Colorizer
Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院）；Harbin Institute of Technology Zhengzhou Research Institute（哈尔滨工业大学郑州研究所）；Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology（深圳先进技术大学人工智能研究所）
Sun Yat-sen University（中山大学）；Guangzhou University（广州大学）；DarkMatter AI
The success of DNNs has driven the extensive applications of person re-identification (ReID) into a new era. However, whether ReID inherits the vulnerability of DNNs remains unexplored. To examine the robustness of ReID systems is rather important because the insecurity of ReID systems may cause severe losses, e.g., the criminals may use the adversarial perturbations to cheat the CCTV systems. In this work, we examine the insecurity of current best-performing ReID models by proposing a learning-to-mis-rank formulation to perturb the ranking of the system output. As the cross-dataset transferab…
We are given a video of a person performing a certain activity, from which we extract a controllable model. The model generates novel image sequences of that person, according to arbitrary user-defined control signals, typically marking the displacement of the moving body. The generated video can have an arbitrary background, and effectively capture both the dynamics and appearance of the person. The method is based on two networks. The first network maps a current pose, and a single-instance control signal to the next pose. The second network maps the current pose, the new pose, and a given b…
