Quater-GCN: Enhancing 3D Human Pose Estimation with Orientation and Semi-Supervised Training

3D human pose estimation aims to reconstruct the human skeleton of all the individuals in a scene by detecting several body joints. The creation of accurate and efficient methods is required for several real-world applications including animation, human–robot interaction, surveillance systems or sports, among many others. However, several obstacles such as occlusions, random camera perspectives, or the scarcity of 3D labelled data, have been hampering the models’ performance and limiting their deployment in real-world scenarios. The higher availability of cameras has led researchers to explore multi-view solutions due to the advantage of being able to exploit different perspectives to reconstruct the pose. Most existing reviews focus mainly on monocular 3D human pose estimation and a comprehensive survey only on multi-view approaches to determine the 3D pose has been missing since 2012. Thus, the goal of this survey is to fill that gap and present an overview of the methodologies related to 3D pose estimation in multi-view settings, understand what were the strategies found to address the various challenges and also, identify their limitations. According to the reviewed articles, it was possible to find that most methods are fully-supervised approaches based on geometric constraints. Nonetheless, most of the methods suffer from 2D pose mismatches, to which the incorporation of temporal consistency and depth information have been suggested to reduce the impact of this limitation, besides working directly with 3D features can completely surpass this problem but at the expense of higher computational complexity. Models with lower supervision levels were identified to overcome some of the issues related to 3D pose, particularly the scarcity of labelled datasets. Therefore, no method is yet capable of solving all the challenges associated with the reconstruction of the 3D pose. Due to the existing trade-off between complexity and performance, the best method depends on the application scenario. Therefore, further research is still required to develop an approach capable of quickly inferring a highly accurate 3D pose with bearable computation cost. To this goal, techniques such as active learning, methods that learn with a low level of supervision, the incorporation of temporal consistency, view selection, estimation of depth information and multi-modal approaches might be interesting strategies to keep in mind when developing a new methodology to solve this task. • First review only on multi-view, multi-modal methods to estimate 3D pose since 2012. • Multi-view allows capturing the full body geometry, making 3D pose estimation easier. • Real-world applications include sports, broadcasting, rehabilitation or animation. • Finding a fast, accurate method with low computational cost remains a challenge. • Multi-modal methods or view selection can lead to an efficient and effective model.

Generalization is very important for pose estimation, especially for 3D pose estimation where small changes in the 2D images could trigger structural changes in the 3D space. To achieve generalization, the system needs to have the capability of detecting estimation errors by double-checking the projection coherence between the 3D and 2D spaces and adapting its network inference process based on this feedback. Current pose estimation is one-time feed-forward and lacks the capability to gather feedback and adapt the inference outcome. To address this problem, we propose to explore the concept of progressive inference where the network learns an observer to continuously detect the prediction error based on constraints matching, as well as an adjuster to refine its inference outcome based on these constraints errors. Within the context of 3D hand pose estimation, we find that this observer-adjuster design is relatively unstable since the observer is operating in the 2D image domain while the adjuster is operating in the 3D domain. To address this issue, we propose to construct two sets of observers-adjusters with complementary constraints from different perspectives. They operate in a dynamic sequential manner controlled by a decision network to progressively improve the 3D pose estimation. We refer to this method as Cross-Constrained Progressive Inference (CCPI). Our extensive experimental results on FreiHAND and HO-3D benchmark datasets demonstrate that the proposed CCPI method is able to significantly improve the generalization capability and performance of 3D hand pose estimation.

Deep 3D human pose estimation: A review

Weakly-supervised pre-training for 3D human pose estimation via perspective knowledge

Human pose capture is essential for sports analysis, enabling precise evaluation of athletes' movements. While deep learning-based human pose estimation (HPE) models from RGB videos have achieved impressive performance on public datasets, their effectiveness in real-world sports scenarios is often hindered by motion blur, occlusions, and domain shifts across different pose representations. Fine-tuning these models can partially alleviate such challenges but typically requires large-scale annotated data and still struggles to generalize across diverse sports environments. To address these limitations, we propose a 2D pose prior-guided refinement approach based on Neural Distance Fields (NDF). Unlike existing approaches that rely solely on angular representations of human poses, we introduce a polar coordinate-based representation that explicitly incorporates joint connection lengths, enabling a more accurate correction of erroneous pose estimations. Additionally, we define a novel non-geodesic distance metric that separates angular and radial discrepancies, which we demonstrate is better suited for polar representations than traditional geodesic distances. To mitigate data scarcity, we develop a gradient-based batch-projection augmentation strategy, which synthesizes realistic pose samples through iterative refinement. Our method is evaluated on a long jump dataset, demonstrating its ability to improve 2D pose estimation across multiple pose representations, making it robust across different domains. Experimental results show that our approach enhances pose plausibility while requiring only limited training data. Code is available at: https://github.com/QGAN2019/polar-NDF.

Automatically estimating 3D human poses in video and inferring their meanings play an essential role in many human-centered automation systems. Existing researches made remarkable progresses by first estimating 2D human joints in video and then reconstructing 3D human pose from the 2D joints. However, mono-directionally reconstructing 3D pose from 2D joints ignores the interaction between information in 3D space and 2D space, losses rich information of original video, therefore limits the ceiling of estimation accuracy. To this end, this paper proposes a bidirectional 2D-3D transformation framework that bidirectionally exchanges 2D and 3D information and utilizes video information to estimate an offset for refining 3D human pose. In addition, a bone-length stability loss is utilized for the purpose of exploring human body structure to make the estimated 3D pose more natural and to further increase the overall accuracy. By evaluation, estimation error of the proposed method, measured by the mean per joint position error (MPJPE), is only 46.5 mm, which is much lower than state-of-the-art methods under the same experimental condition. The improvement on accuracy will make machines to better understand human poses for building superior human-centered automation systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the demand of human-centered automation systems needing to accurately understand human poses. Existing approaches mainly focus on inferring 3D human pose from 2D joints mono-directionally. Although they made remarkable contributions to estimating 3D human pose in such a mono-directional way, we found that they ignore the 2D-3D interaction and do not use original video when inferring 3D pose from 2D joints. This paper therefore suggests a bidirectional 2D-3D transformation that exchanges 2D and 3D information and utilizes video information to estimate more accurate 3D human pose for human-centered automation systems. This work is a pioneering attempt of interactively using 2D and 3D information for more accurate estimation of human pose. Benefited from the state-of-the-art accuracy, the proposed approach is expected to make significant contributions to many human-centered automation systems, such as human-machine interaction, biomimetic manipulation, and automatic surveillance systems.

We present a deployment friendly, fast bottom-up framework for multi-person 3D human pose estimation. We adopt a novel neural representation of multi-person 3D pose which unifies the position of person instances with their corresponding 3D pose representation. This is realized by learning a generative pose embedding which not only ensures plausible 3D pose predictions, but also eliminates the usual keypoint grouping operation as employed in prior bottom-up approaches. Further, we propose a practical deployment paradigm where paired 2D or 3D pose annotations are unavailable. In the absence of any paired supervision, we leverage a frozen network, as a teacher model, which is trained on an auxiliary task of multi-person 2D pose estimation. We cast the learning as a cross-modal alignment problem and propose training objectives to realize a shared latent space between two diverse modalities. We aim to enhance the model’s ability to perform beyond the limiting teacher network by enriching the latent-to-3D pose mapping using artificially synthesized multi-person 3D scene samples. Our approach not only generalizes to in-the-wild images, but also yields a superior trade-off between speed and performance, compared to prior top-down approaches. Our approach also yields state-of-the-art multi-person 3D pose estimation performance among the bottom-up approaches under consistent supervision levels.

Multi-view 3D human pose estimation (MHPE) is an important research task in computer vision. To maintain consistency during the data collection, hardware synchronization devices are commonly used to connect cameras, ensuring that images from different views are captured simultaneously. However, synchronizing with extra devices has two apparent limitations: the hardware is i) usually expensive and ii) less flexible for deployment in outdoor open scenarios. Suppose the model can improve its tolerance for the time differences in multi-view image capture. In that case, the difficulty and cost of deployment will be greatly reduced, and MHPE will become more widespread. In this paper, we try to answer how to build a model that performs pose estimation directly using ''weakly synchronized images" from multiple views, where the captured images shift from each other within a frame. To this end, we introduce a new multi-view 3D human pose estimation task given weakly synchronized image inputs. Apart from existing well-synchronized datasets, we present the first weakly synchronized dataset comprising 800k images. Thereon, we propose SyncDiffPose, a novel model based on the diffusion method for pose estimation to denoise the error in such data. By combining simple synchronization strategies, e.g., the timer method, our approach can perform pose estimation without hardware calibration.

3D human pose estimation is a fundamental task in computer vision. However, most of the related works focus on recovering human joint positions, which provides sparse and insufficient pose information for many applications like 3D avatar animation. Therefore, this paper presents a deep network for recovering joint angles from 3D joint positions, which learns the prior dependence between them. We test the validity and robustness of our method. We also discuss some details in designing and training the network. Our method is simple, effective and extensive. It can be combined with work of 3D human pose estimation that predict 3D joint positions from image or depth data to produce more detailed and natural poses. It builds a map between two joint sets with different numbers of joints, which provides a framework to unify multiple datasets for human pose estimation with different annotation formats.

Pose estimation which is regard as the cross-technology of computer vision and pattern recognition, and an important prerequisite for human behavior understanding. Human pose estimation which use the probability theory, machine learning, pattern recognition, graph theory and other theories to get the position, the deflection angle of the various parts of the body. Then make the detection and estimation parameters for the human body pose. When the image has interference in the background, color and scale changed, human pose complex, self-occlusion and interpersonal interaction occlusion may make the precision and accuracy of pose estimation face great challenge. Thus, according to the above problems, this paper use the advanced model of the human body as contour model to descript the complex pose, in order to make the model more accurate and suitable for various human pose, we preclustering the human body pose of the training samples before we trained the model and in order to ensure the accuracy of the pose we use robust segmentation of multi-view with a novel shape prior. The experiment shows that the algorithm performs better than the classic algorithm on the public datasets.

Human Pose Estimation (HPE) is defined as the problem of human joints’ localization (also known as keypoints: elbows, wrists, etc.) in images or videos. It is also defined as the search for a specific pose in space of all articulated joints. HPE has recently received significant attention from the scientific community. The main reason behind this trend is that pose estimation is considered as a key step for many computer vision tasks. Although many approaches have reported promising results, this domain remains largely unsolved due to several challenges such as occlusions, small and barely visible joints, and variations in clothing and lighting. In the last few years, the power of deep neural networks has been demonstrated in a wide variety of computer vision problems and especially the HPE task. In this context, we present in this paper a Deep Full-Body-HPE (DFB-HPE) approach from RGB images only. Based on ConvNets, fifteen human joint positions are predicted and can be further exploited for a large range of applications such as gesture recognition, sports performance analysis, or human-robot interaction. To evaluate the proposed deep pose estimation model, we apply it to recognize the daily activities of a person in an unconstrained environment. Therefore, the extracted features, represented by deep estimated poses, are fed to an SVM classifier. To validate the proposed architecture, our approach is tested on two publicly available benchmarks for pose estimation and activity recognition, namely the J-HMDBand CAD-60datasets. The obtained results demonstrate the efficiency of the proposed method based on ConvNets and SVM and prove how deep pose estimation can improve the recognition accuracy. By means of comparison with state-of-the-art methods, we achieve the best HPE performance, as well as the best activity recognition precision on the CAD-60 dataset.

Human pose estimation (HPE) is most dominant research areas in Computer Vision (CV) field. This technology will have huge implications. The significant applications include human computer interaction, activity recognition, video surveillance, motion recognition, etc. Different types of pose estimation are used for estimating the number of persons who are tracked. In this paper we are focusing on single 2D pose estimation. Present trends of pose estimation uses CNN based architectures for HPE and post statistical methods. In this work we propose a monadic frame work for both HPE and post processing into a single stage. It produces a human pose skeleton for a single two-dimensional image and videos. Here, take different modalities such as static image, static video and live video to generate different skeletons for SHPE. In this work we use pre-trained tensor flow based CNN model for pose estimation and also it produce better results compared to state of art in terms of performance metrics as PCP@.5, PCK@.5, and PCKh@.2.

SkeletonPose: Exploiting human skeleton constraint for 3D human pose estimation

We present the first method to perform automatic 3D pose, shape and texture capture of animals from images acquired in-the-wild. In particular, we focus on the problem of capturing 3D information about Grevy's zebras from a collection of images. The Grevy's zebra is one of the most endangered species in Africa, with only a few thousand individuals left. Capturing the shape and pose of these animals can provide biologists and conservationists with information about animal health and behavior. In contrast to research on human pose, shape and texture estimation, training data for endangered species is limited, the animals are in complex natural scenes with occlusion, they are naturally camouflaged, travel in herds, and look similar to each other. To overcome these challenges, we integrate the recent SMAL animal model into a network-based regression pipeline, which we train end-to-end on synthetically generated images with pose, shape, and background variation. Going beyond state-of-the-art methods for human shape and pose estimation, our method learns a shape space for zebras during training. Learning such a shape space from images using only a photometric loss is novel, and the approach can be used to learn shape in other settings with limited 3D supervision. Moreover, we couple 3D pose and shape prediction with the task of texture synthesis, obtaining a full texture map of the animal from a single image. We show that the predicted texture map allows a novel per-instance unsupervised optimization over the network features. This method, SMALST (SMAL with learned Shape and Texture) goes beyond previous work, which assumed manual keypoints and/or segmentation, to regress directly from pixels to 3D animal shape, pose and texture. Code and data are available at https://github.com/silviazuffi/smalst.

Computer vision/deep learning-based 3D human pose estimation methods aim to localize human joints from images and videos. Pose representation is normally limited to 3D joint positional/translational degrees of freedom (3DOFs), however, a further three rotational DOFs (6DOFs) are required for many potential biomechanical applications. Positional DOFs are insufficient to analytically solve for joint rotational DOFs in a 3D human skeletal model. Therefore, we propose a temporal inverse kinematics (IK) optimization technique to infer joint orientations throughout a biomechanically informed, and subject-specific kinematic chain. For this, we prescribe link directions from a position-based 3D pose estimate. Sequential least squares quadratic programming is used to solve a minimization problem that involves both frame-based pose terms, and a temporal term. The solution space is constrained using joint DOFs, and ranges of motion (ROMs). We generate 3D pose motion sequences to assess the IK approach both for general accuracy, and accuracy in boundary cases. Our temporal algorithm achieves 6DOF pose estimates with low Mean Per Joint Angular Separation (MPJAS) errors (3.7°/joint overall, &amp; 1.6°/joint for lower limbs). With frame-by-frame IK we obtain low errors in the case of bent elbows and knees, however, motion sequences with phases of extended/straight limbs results in ambiguity in twist angle. With temporal IK, we reduce ambiguity for these poses, resulting in lower average errors. Code and supplementary material are available.