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 generate high-quality images. Specifically, we first generate the target semantic map under the guidence of the target pose, which can provide more accurate pose representation and structural constraints during the generation process. Then, decomposed attribute encoder is used to extract the component features, which not only helps to establish a more accurate dense correspondence, but also realizes the clothing-guided person generation. After that, we will establish a dense correspondence between the target pose and the source image within the sharded domain. The source image feature is warped according to the dense correspondence to flexibly account for deformations. Finally, the network renders image based on the warped source image feature and the target pose. Experimental results show that our method is superior to state-of-the-art methods in pose-guided person generation and its effectiveness in clothing-guided person generation.
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 H…
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…
Dynamic Uncertainty Learning with Noisy Correspondence for Text-Based Person Search
Zhejiang University（浙江大学）；Beijing University of Posts Telecommunications（北京邮电大学）；Beijing Forestry University（北京林业大学）；Northwest Normal University（西北师范大学）
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）
ByteDance Inc（字节跳动公司）；National University of Singapore（新加坡国立大学）
We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses. To achieve such high level of disentangled control, we first explicitly define a novel semantic signed distance function (SDF) around a head geometry (FLAME) conditioned on the control parameters. This semantic SDF allows us to build a differentiable volumetric corres…
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Aalto University（阿尔托大学）；University of Central Florida（中佛罗里达大学）；Linköping University（林雪平大学）
The pose-guided person image generation task requires synthesizing photorealistic images of humans in arbitrary poses. The existing approaches use generative adversarial networks that do not necessarily maintain realistic textures or need dense correspondences that struggle to handle complex deformations and severe occlusions. In this work, we show how denoising diffusion models can be applied for high-fidelity person image synthesis with strong sample diversity and enhanced mode coverage of the learnt data distribution. Our proposed Person Image Diffusion Model (PIDM) disintegrates the comple…
CFCS, Peking University（北京大学CFCS）；University College London（伦敦大学学院）；Tel Aviv University（特拉维夫大学）；AICFVE, Beijing Film Academy（北京电影学院AICFVE）；Adobe Research（奥多比研究院）
In this paper, we leverage advances in neural networks towards forming a neural rendering for controllable image generation, and thereby bypassing the need for detailed modeling in conventional graphics pipeline. To this end, we present Neural Graphics Pipeline (NGP), a hybrid generative model that brings together neural and traditional image formation models. NGP decomposes the image into a set of interpretable appearance feature maps, uncovering direct control handles for controllable image generation. To form an image, NGP generates coarse 3D models that are fed into neural rendering module…
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）
Controllable-Continuous Color Editing in Diffusion Model via Color Mapping
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）；SketchX, CVSSP, University of Surrey（SketchX、CVSSP、 Surrey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…
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…
The Chinese University of Hong Kong（香港中文大学）；SmartMore（思谋科技）
Current 3D scene stylization methods transfer textures and colors as styles using arbitrary style references, lacking meaningful semantic correspondences. We introduce Reference-Based Non-Photorealistic Radiance Fields (Ref-NPR) to address this limitation. This controllable method stylizes a 3D scene using radiance fields with a single stylized 2D view as a reference. We propose a ray registration process based on the stylized reference view to obtain pseudo-ray supervision in novel views. Then we exploit semantic correspondences in content images to fill occluded regions with perceptually sim…
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…
Video annotation is expensive and time consuming. Consequently, datasets for multi-person pose estimation and tracking are less diverse and have more sparse annotations compared to large scale image datasets for human pose estimation. This makes it challenging to learn deep learning based models for associating keypoints across frames that are robust to nuisance factors such as motion blur and occlusions for the task of multi-person pose tracking. To address this issue, we propose an approach that relies on keypoint correspondences for associating persons in videos. Instead of training the net…
Shanghai Jiao Tong University（上海交通大学）；Zhengzhou University（郑州大学）；Nanjing University（南京大学）；Microsoft Research Asia（微软亚洲研究院）
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global-based matching process. It integrates a global matching cons…
We address the problem of visible-infrared person re-identification (VI-reID), that is, retrieving a set of person images, captured by visible or infrared cameras, in a cross-modal setting. Two main challenges in VI-reID are intra-class variations across person images, and cross-modal discrepancies between visible and infrared images. Assuming that the person images are roughly aligned, previous approaches attempt to learn coarse image- or rigid part-level person representations that are discriminative and generalizable across different modalities. However, the person images, typically cropped…
Shanghai Jiao Tong University（上海交通大学）；South China University of Technology（华南理工大学）；Temple University（天普大学）；Tsinghua University（清华大学）；Tencent（腾讯）
In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification. Unlike existing methods, the GCT model formulates person re-identification as an off-line graph matching and on-line correspondence transferring problem. In specific, during training, the GCT model aims to learn off-line a set of correspondence templates from positive training pairs with various pose-pair configurations via patch-wise graph matching. During testing, for each pair of test samples, we select a few training pairs with the most similar pose-pair configurations as references, and …
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…
Spatial misalignment caused by variations in poses and viewpoints is one of the most critical issues that hinders the performance improvement in existing person re-identification (Re-ID) algorithms. To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID. Specifically, we propose to establish the patch-wise correspondences of positive training pairs via graph matching. By exploiting both spatial and visual contexts of human appearance in graph matching, meaningful semantic correspondences c…
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…
