Controllable text-to-image (T2I) diffusion models have shown impressive performance in generating high-quality visual content through the incorporation of various conditions. Current methods, however, exhibit limited performance when guided by skeleton human poses, especially in complex pose conditions such as side or rear perspectives of human figures. To address this issue, we present Stable-Pose, a novel adapter model that introduces a coarse-to-fine attention masking strategy into a vision Transformer (ViT) to gain accurate pose guidance for T2I models. Stable-Pose is designed to adeptly handle pose conditions within pre-trained Stable Diffusion, providing a refined and efficient way of aligning pose representation during image synthesis. We leverage the query-key self-attention mechanism of ViTs to explore the interconnections among different anatomical parts in human pose skeletons. Masked pose images are used to smoothly refine the attention maps based on target pose-related features in a hierarchical manner, transitioning from coarse to fine levels. Additionally, our loss function is formulated to allocate increased emphasis to the pose region, thereby augmenting the model's precision in capturing intricate pose details. We assessed the performance of Stable-Pose across five public datasets under a wide range of indoor and outdoor human pose scenarios. Stable-Pose achieved an AP score of 57.1 in the LAION-Human dataset, marking around 13% improvement over the established technique ControlNet. The project link and code is available at https://github.com/ai-med/StablePose.
Samsung AI Center（三星人工智能中心）；Skolkovo Institute of Science and Technology (Skoltech)（斯科尔科沃科学技术学院（Skoltech））
We present a new deep learning approach to pose-guided resynthesis of human photographs. At the heart of the new approach is the estimation of the complete body surface texture based on a single photograph. Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body. Rather than working directly with colors of texture elements, the inpainting network estimates an appropriate source location in the input image for each element of the body surface. This correspondence field between the input image and the …
Beuth University of Applied Sciences Berlin（柏林博伊特应用科学大学）
We propose a novel approach for the synthesis of sign language videos using GANs. We extend the previous work of Stoll et al. by using the human semantic parser of the Soft-Gated Warping-GAN from to produce photorealistic videos guided by region-level spatial layouts. Synthesizing target poses improves performance on independent and contrasting signers. Therefore, we have evaluated our system with the highly heterogeneous MS-ASL dataset with over 200 signers resulting in a SSIM of 0.893. Furthermore, we introduce a periodic weighting approach to the generator that reactivates the training and …
KU-Leuven/PSI（比利时鲁汶大学/PSI）；TRACE (Toyota Res in Europe)（TRACE（欧洲丰田研究院））；IMEC（IMEC（微电子研究中心））；Max Planck Institute for Informatics（马克斯·普朗克计算机科学研究所）；Saarland Informatics Campus（萨尔兰信息学园区）；ETH Zurich（苏黎世联邦理工学院）
This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG$^2$ utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose. The second stage then refines the initial and blurry result by training a U-Net-like generator in a…
Due to the emergence of Generative Adversarial Networks, video synthesis has witnessed exceptional breakthroughs. However, existing methods lack a proper representation to explicitly control the dynamics in videos. Human pose, on the other hand, can represent motion patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance. In this paper, we propose a pose guided method to synthesize human videos in a disentangled way: plausible motion prediction and coherent appearance generation. In the first stage, a Pose Sequence Generative Adversarial Network …
In this paper, we propose a novel approach to solve the pose guided person image generation task. We assume that the relation between pose and appearance information can be described by a simple matrix operation in hidden space. Based on this assumption, our method estimates a pose-invariant feature matrix for each identity, and uses it to predict the target appearance conditioned on the target pose. The estimation process is formulated as a p-norm regression problem in hidden space. By utilizing the differentiation of the solution of this regression problem, the parameters of the whole framew…
We propose a novel pose-guided appearance transfer network for transferring a given reference appearance to a target pose in unprecedented image resolution (1024 * 1024), given respectively an image of the reference and target person. No 3D model is used. Instead, our network utilizes dense local descriptors including local perceptual loss and local discriminators to refine details, which is trained progressively in a coarse-to-fine manner to produce the high-resolution output to faithfully preserve complex appearance of garment textures and geometry, while hallucinating seamlessly the transfe…
Sun Yat-sen University（中山大学）；South China Normal University（华南师范大学）
Virtual try-on system under arbitrary human poses has huge application potential, yet raises quite a lot of challenges, e.g. self-occlusions, heavy misalignment among diverse poses, and diverse clothes textures. Existing methods aim at fitting new clothes into a person can only transfer clothes on the fixed human pose, but still show unsatisfactory performances which often fail to preserve the identity, lose the texture details, and decrease the diversity of poses. In this paper, we make the first attempt towards multi-pose guided virtual try-on system, which enables transfer clothes on a pers…
Virginia Tech（弗吉尼亚理工大学）；Kuwait University（科威特大学）；Adobe Research（Adobe 研究院）；University of Maryland College Park（马里兰大学帕克分校）
We present an algorithm for re-rendering a person from a single image under arbitrary poses. Existing methods often have difficulties in hallucinating occluded contents photo-realistically while preserving the identity and fine details in the source image. We first learn to inpaint the correspondence field between the body surface texture and the source image with a human body symmetry prior. The inpainted correspondence field allows us to transfer/warp local features extracted from the source to the target view even under large pose changes. Directly mapping the warped local features to an RG…
North Carolina State University（北卡罗来纳州立大学）；JD.com American Technologies（京东美国技术公司）；OPPO US Research Center（OPPO美国研究中心）
Generating a photorealistic image with intended human pose is a promising yet challenging research topic for many applications such as smart photo editing, movie making, virtual try-on, and fashion display. In this paper, we present a novel deep generative model to transfer an image of a person from a given pose to a new pose while keeping fashion item consistent. In order to formulate the framework, we employ one generator and two discriminators for image synthesis. The generator includes an image encoder, a pose encoder and a decoder. The two encoders provide good representation of visual an…
The Hong Kong University of Science and Technology（香港科技大学）；Microsoft Research Asia（微软亚洲研究院）
We propose pose-guided multiplane image (MPI) synthesis which can render an animatable character in real scenes with photorealistic quality. We use a portable camera rig to capture the multi-view images along with the driving signal for the moving subject. Our method generalizes the image-to-image translation paradigm, which translates the human pose to a 3D scene representation -- MPIs that can be rendered in free viewpoints, using the multi-views captures as supervision. To fully cultivate the potential of MPI, we propose depth-adaptive MPI which can be learned using variable exposure images…
Pose Guided Person Image Generation (PGPIG) is the task of transforming a person image from the source pose to a given target pose. Most of the existing methods only focus on the ill-posed source-to-target task and fail to capture reasonable texture mapping. To address this problem, we propose a novel Dual-task Pose Transformer Network (DPTN), which introduces an auxiliary task (i.e., source-to-source task) and exploits the dual-task correlation to promote the performance of PGPIG. The DPTN is of a Siamese structure, containing a source-to-source self-reconstruction branch, and a transformatio…
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…
This paper studies the task of full generative modelling of realistic images of humans, guided only by coarse sketch of the pose, while providing control over the specific instance or type of outfit worn by the user. This is a difficult problem because input and output domain are very different and direct image-to-image translation becomes infeasible. We propose an end-to-end trainable network under the generative adversarial framework, that provides detailed control over the final appearance while not requiring paired training data and hence allows us to forgo the challenging problem of fitti…
Pose-guided person image generation and animation aim to transform a source person image to target poses. These tasks require spatial manipulation of source data. However, Convolutional Neural Networks are limited by the lack of ability to spatially transform the inputs. In this paper, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level. This framework first estimates global flow fields between sources and targets. Then, corresponding local source feature patches are sampled with content-aware local attention coefficients. We show tha…
University of California, Irvine（加州大学欧文分校）；Tencent America（腾讯美国）；Amazon（亚马逊）；National Chiao Tung University（国立交通大学）
Estimating3D hand poses from RGB images is essentialto a wide range of potential applications, but is challengingowing to substantial ambiguity in the inference of depth in-formation from RGB images. State-of-the-art estimators ad-dress this problem by regularizing3D hand pose estimationmodels during training to enforce the consistency betweenthe predicted3D poses and the ground-truth depth maps.However, these estimators rely on both RGB images and thepaired depth maps during training. In this study, we proposea conditional generative adversarial network (GAN) model,called Depth-image Guided G…
DAC-Pose: Dual-Agent Collaborative Framework for Pose-Guided Human Generation
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Shenzhen University of Advanced Technology（深圳理工大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
CRCV, University of Central Florida（中佛罗里达大学计算机视觉研究中心）；National University of Singapore（新加坡国立大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
We focus on the problem of novel-view human action synthesis. Given an action video, the goal is to generate the same action from an unseen viewpoint. Naturally, novel view video synthesis is more challenging than image synthesis. It requires the synthesis of a sequence of realistic frames with temporal coherency. Besides, transferring the different actions to a novel target view requires awareness of action category and viewpoint change simultaneously. To address these challenges, we propose a novel framework named Pose-guided Action Separable Generative Adversarial Net (PAS-GAN), which utili…
This paper presents a novel method to manipulate the visual appearance (pose and attribute) of a person image according to natural language descriptions. Our method can be boiled down to two stages: 1) text guided pose generation and 2) visual appearance transferred image synthesis. In the first stage, our method infers a reasonable target human pose based on the text. In the second stage, our method synthesizes a realistic and appearance transferred person image according to the text in conjunction with the target pose. Our method extracts sufficient information from the text and establishes …
HyperMotionX: The Dataset and Benchmark with DiT-Based Pose-Guided Human Image Animation of Complex Motions
Bournemouth University（伯恩茅斯大学）；vivo BlueImage Lab, vivo Mobile Communication Co., Ltd（vivo 蓝影实验室，vivo 通信有限公司）；Peking University（北京大学）；National Cheng-Kung University（国立成功大学）
3D Hand Mesh-Guided AI-Generated Malformed Hand Refinement with Hand Pose Transformation via Diffusion Model
Department of Computer and Information Science, University of Macau, Macau, China（计算机与信息科学系，澳门大学，澳门，中国）；ShanghaiTech University, Shanghai, China（上海科技大学，上海，中国）
State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）；University of Leeds（利兹大学）；Clemson University（克莱姆森大学）
Generating new images with desired properties (e.g. new view/poses) from source images has been enthusiastically pursued recently, due to its wide range of potential applications. One way to ensure high-quality generation is to use multiple sources with complementary information such as different views of the same object. However, as source images are often misaligned due to the large disparities among the camera settings, strong assumptions have been made in the past with respect to the camera(s) or/and the object in interest, limiting the application of such techniques. Therefore, we propose…
MultiAnimate: Pose-Guided Image Animation Made Extensible
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；ShanghaiTech University（上海科技大学）；Shanghai Jiao Tong University（上海交通大学）
Sun Yat-sen University（中山大学）；South China Normal University（华南师范大学）
Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric change…
One-Shot Learning for Pose-Guided Person Image Synthesis in the Wild
University of Electronic Science and Technology of China（电子科技大学）；China Mobile Zijin (Jiangsu) Innovation Research Institute Co., Ltd（中国移动紫金（江苏）创新研究院有限公司）；Zhejiang Sci-Tech University（浙江理工大学）；Jilin University（吉林大学）；Huawei Canada（华为加拿大公司）；Nanjing University（南京大学）；China Mobile Research Institute（中国移动研究院）
Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（机器智能与先进计算教育部重点实验室）；Pazhou Lab (HuangPu)（琶洲实验室（黄埔））
Queensland University of Technology (QUT)（昆士兰科技大学）
Person re-identification (re-ID) concerns the matching of subject images across different camera views in a multi camera surveillance system. One of the major challenges in person re-ID is pose variations across the camera network, which significantly affects the appearance of a person. Existing development data lack adequate pose variations to carry out effective training of person re-ID systems. To solve this issue, in this paper we propose an end-to-end pose-driven attention-guided generative adversarial network, to generate multiple poses of a person. We propose to attentively learn and tr…
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）
University of British Columbia（不列颠哥伦比亚大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；University of Trento（特伦托大学）
Generation of realistic high-resolution videos of human subjects is a challenging and important task in computer vision. In this paper, we focus on human motion transfer - generation of a video depicting a particular subject, observed in a single image, performing a series of motions exemplified by an auxiliary (driving) video. Our GAN-based architecture, DwNet, leverages dense intermediate pose-guided representation and refinement process to warp the required subject appearance, in the form of the texture, from a source image into a desired pose. Temporal consistency is maintained by further …
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer
Zhejiang University（浙江大学）；Alibaba Group（阿里巴巴集团）；Institute of High Performance Computing (IHPC)（高性能计算研究所）；Centre for Frontier AI Research (CFAR)（前沿人工智能研究中心）
Ping An Technology (Shenzhen) Co., Ltd.（平安科技（深圳）有限公司）；University of Science and Technology of China（中国科学技术大学）；Shanghai International Studies University（上海外国语大学）
Pose Guided Human Image Synthesis (PGHIS) is a challenging task of transforming a human image from the reference pose to a target pose while preserving its style. Most existing methods encode the texture of the whole reference human image into a latent space, and then utilize a decoder to synthesize the image texture of the target pose. However, it is difficult to recover the detailed texture of the whole human image. To alleviate this problem, we propose a method by decoupling the human body into several parts (\eg, hair, face, hands, feet, \etc) and then using each of these parts to guide th…
Indian Institute of Space Science and Technology（印度空间科学与技术学院）
Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. Existing works use a complicated pose transformation model with various additional features such as foreground segmentation, human body parsing etc. to achieve robustness that leads to computational overhead. In this work, we propose a simple yet effective pose transformation GAN…
Human pose transfer, which aims at transferring the appearance of a given person to a target pose, is very challenging and important in many applications. Previous work ignores the guidance of pose features or only uses local attention mechanism, leading to implausible and blurry results. We propose a new human pose transfer method using a generative adversarial network (GAN) with simplified cascaded blocks. In each block, we propose a pose-guided non-local attention (PoNA) mechanism with a long-range dependency scheme to select more important regions of image features to transfer. We also des…
Pose guided synthesis aims to generate a new image in an arbitrary target pose while preserving the appearance details from the source image. Existing approaches rely on either hard-coded spatial transformations or 3D body modeling. They often overlook complex non-rigid pose deformation or unmatched occluded regions, thus fail to effectively preserve appearance information. In this paper, we propose an unsupervised pose flow learning scheme that learns to transfer the appearance details from the source image. Based on such learned pose flow, we proposed GarmentNet and SynthesisNet, both of whi…
Hand pose estimation from a single depth image is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural network, accurate hand pose estimation is still a challenging problem. In this paper we propose a Pose guided structured Region Ensemble Network (Pose-REN) to boost the performance of hand pose estimation. The proposed method extracts regions from the feature maps of convolutional neural network under the guide of an initially estimated pose, generating more optimal and representative features for hand …
Beijing Laboratory of Advanced Information Networks（北京先进信息网络实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Horizon Robotics（地平线机器人）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Human pose estimation has witnessed a significant advance thanks to the development of deep learning. Recent human pose estimation approaches tend to directly predict the location heatmaps, which causes quantization errors and inevitably deteriorates the performance within the reduced network output. Aim at solving it, we revisit the heatmap-offset aggregation method and propose the Offset-guided Network (OGN) with an intuitive but effective fusion strategy for both two-stages pose estimation and Mask R-CNN. For two-stages pose estimation, a greedy box generation strategy is also proposed to k…
UBTECH Sydney AI Center, The University of Sydney（悉尼大学优必选悉尼人工智能中心）；Dalian University of Technology（大连理工大学）；Pengcheng Lab（鹏城实验室）
Occluded person re-identification is a challenging task as the appearance varies substantially with various obstacles, especially in the crowd scenario. To address this issue, we propose a Pose-guided Visible Part Matching (PVPM) method that jointly learns the discriminative features with pose-guided attention and self-mines the part visibility in an end-to-end framework. Specifically, the proposed PVPM includes two key components: 1) pose-guided attention (PGA) method for part feature pooling that exploits more discriminative local features; 2) pose-guided visibility predictor (PVP) that esti…
Qualcomm AI Research（高通人工智能研究院）；Qualcomm Technologies, Inc.（高通技术公司）；Shanghai Jiao Tong University（上海交通大学）
There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies. However, existing transformer-based methods treat body joints as equally important inputs and ignore the prior knowledge of human skeleton topology in the self-attention mechanism. To tackle this issue, in this paper, we propose a Pose-Oriented Transformer (POT) with uncertainty guided refinement for 3D HPE. Specifically, we first develop novel pose-oriented self-attention mechanism and distance-related position embedding…
We propose POse-guided SElective Fusion (POSEFusion), a single-view human volumetric capture method that leverages tracking-based methods and tracking-free inference to achieve high-fidelity and dynamic 3D reconstruction. By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions. We formulate the keyframe s…
SAM-pose2seg: Pose-Guided Human Instance Segmentation in Crowds
Visual Recognition Group（视觉识别组）；Department of Cybernetics（cybernetics系）；Faculty of Electrical Engineering（电气工程系）；Czech Technical University in Prague（布拉格捷克技术大学）
