Pose estimation of industrial objects towards robot operation

Deep 3D human pose estimation: A review

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.

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

Monocular 3D human pose estimation is challenging due to depth ambiguity. Convolution-based and Graph-Convolution-based methods have been developed to extract 3D information from temporal cues in motion videos. Typically, in the lifting-based methods, most recent works adopt the transformer to model the temporal relationship of 2D keypoint sequences. These previous works usually consider all the joints of a skeleton as a whole and then calculate the temporal attention based on the overall characteristics of the skeleton. Nevertheless, the human skeleton exhibits obvious part-wise inconsistency of motion patterns. It is therefore more appropriate to consider each part's temporal behaviors separately. To deal with such part-wise motion inconsistency, we propose the Part Aware Temporal Attention module to extract the temporal dependency of each part separately. Moreover, the conventional attention mechanism in 3D pose estimation usually calculates attention within a short time interval. This indicates that only the correlation within the temporal context is considered. Whereas, we find that the part-wise structure of the human skeleton is repeating across different periods, actions, and even subjects. Therefore, the part-wise correlation at a distance can be utilized to further boost 3D pose estimation. We thus propose the Part Aware Dictionary Attention module to calculate the attention for the part-wise features of input in a dictionary, which contains multiple 3D skeletons sampled from the training set. Extensive experimental results show that our proposed part aware attention mechanism helps a transformer-based model to achieve state-of-the-art 3D pose estimation performance on two widely used public datasets. The codes and the trained models are released at https://github.com/thuxyz19/3D-HPE-PAA.

It is widely shown that Convolutional Neural Networks (CNNs) are vulnerable to adversarial examples on most recognition tasks, such as image classification and segmentation. However, few work studies the more complicated task – 3D human pose estimation. This task often requires large-scale datasets, specialized network architectures, and it can be solved either from single-view RGB images or from multi-view RGB images. In this paper, we make the first attempt to investigate the robustness of current state-of-the-art 3D human pose estimation methods. To this end, we build four representative baseline models, where most of the current methods can be generally classified as one of them. Furthermore, we design targeted adversarial attacks to detect whether 3D pose estimators are robust to different camera parameters. For different types of methods, we present a comprehensive study of their robustness on the large-scale Human3.6M benchmark. Our work shows that different methods vary significantly in their resistance to adversarial attacks. Through extensive experiments, we show that multi-view 3D pose estimators can be more vulnerable to adversarial examples. We believe that our efforts can shed light on future works to design more robust 3D human pose estimators.

We propose UniPose+, a unified framework for 2D and 3D human pose estimation in images and videos. The UniPose+ architecture leverages multi-scale feature representations to increase the effectiveness of backbone feature extractors, with no significant increase in network size and no postprocessing. Current pose estimation methods heavily rely on statistical postprocessing or predefined anchor poses for joint localization. The UniPose+ framework incorporates contextual information across scales and joint localization with Gaussian heatmap modulation at the decoder output to estimate 2D and 3D human pose in a single stage with state-of-the-art accuracy, without relying on predefined anchor poses. The multi-scale representations allowed by the waterfall module in the UniPose+ framework leverage the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Our results on multiple datasets demonstrate that UniPose+, with a HRNet, ResNet or SENet backbone and waterfall module, is a robust and efficient architecture for single person 2D and 3D pose estimation in single images and videos.

Object recognition and pose estimation are critical components in autonomous robot manipulation systems, playing a crucial role in enabling robots to interact effectively with the environment. During actual execution, the robot must recognize the object in the current scene, estimate its pose, and then select a feasible grasp pose from the pre-defined grasp configurations. While most existing methods primarily focus on pose estimation, they often neglect the graspability and reachability aspects. This oversight can lead to inefficiencies and failures during execution. In this study, we introduce an innovative graspability-aware object pose estimation framework. Our proposed approach not only estimates the poses of multiple objects in clustered scenes but also identifies graspable areas. This enables the system to concentrate its efforts on specific points or regions of an object that are suitable for grasping. It leverages both depth and color images to extract geometric and appearance features. To effectively combine these diverse features, we have developed an adaptive fusion module. In addition, the fused features are further enhanced through a graspability-aware feature enhancement module. The key innovation of our method lies in improving the discriminability and robustness of the features used for object pose estimation. We have achieved state-of-the-art results on public datasets when compared to several baseline methods. In real robot experiments conducted on a Franka Emika robot arm equipped with an Intel Realsense camera and a two-finger gripper, we consistently achieved high success rates, even in cluttered scenes.

Validity of an artificial intelligence, human pose estimation model for measuring single-leg squat kinematics

This work focuses on the problem of 6D pose estimation for novel objects when a reference 3D model or posed reference images are not available. While existing methods can estimate the precise 6D pose of objects, they heavily rely on curated CAD models or reference images, the preparation of which is a time-consuming and labor-intensive process. Moreover, in real-world scenarios, 3D models or reference images may not be available in advance and instant robot reaction is desired. In this work, we propose a novel framework named HIPPo, which eliminates the need for curated CAD models and reference images by harnessing image-to-3D priors from Diffusion Models, enabling model-free zero-shot 6D pose estimation. Specifically, we construct HIPPo Dreamer, a rapid image-to-mesh model built on a multiview Diffusion Model and a 3D reconstruction foundation model. Our HIPPo Dreamer can generate a 3D mesh of any unseen objects from a single glance in just a few seconds. Then, as more observations are acquired, we propose to continuously refine the diffusion prior mesh model by joint optimization of object geometry and appearance. This is achieved by a measurement-guided scheme that gradually replaces the plausible diffusion priors with more reliable online observations. Consequently, HIPPo can instantly estimate and track the 6D pose of a novel object and maintain a complete mesh for immediate robotic applications. Thorough experiments on various benchmarks show that HIPPo outperforms state-of-the-art methods in 6D object pose estimation when prior reference images are limited.

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.

Template matching has been shown to accurately estimate the pose of a new object given a limited number of samples. However, pose estimation of occluded objects is still challenging. Furthermore, many robot application domains encounter texture-less objects for which depth images are more suitable than color images. In this paper, we propose a novel framework, Multi-Task Template Matching (MTTM), that finds the nearest template of a target object from a depth image while predicting segmentation masks and a pose transformation between the template and a detected object in the scene using the same feature map of the object region. The proposed feature comparison network computes segmentation masks and pose predictions by comparing feature maps of templates and cropped features of a scene. The segmentation result from this network improves the robustness of the pose estimation by excluding points that do not belong to the object. Experimental results show that MTTM outperforms baseline methods for segmentation and pose estimation of occluded objects despite using only depth images.

Real-time pose estimation of rigid objects in heavily cluttered environments

In the presented work, innovative low-sampling lightweight architecture is proposed to solve the task of 3D human pose estimation based on 2D key points. The approach introduces specialized trainable pose encodings designed for 3D pose estimation tasks, which are used in conjunction with traditional pose encodings to represent the input data. The architecture of the method includes multilevel feature processing and their adaptive association using a spatial attention mechanism, which allows to enhance relevant features. Experiments conducted on standard test datasets confirmed the effectiveness of the proposed method: a mean joint position error (MPJPE) value of 42.1 was achieved, which exceeds the results of existing approaches.

In this paper, we propose 3DBodyTex.Pose, a dataset that addresses the task of 3D human pose estimation in-the-wild. Generalization to in-the-wild images remains limited due to the lack of adequate datasets. Existent ones are usually collected in indoor controlled environments where motion capture systems are used to obtain the 3D ground-truth annotations of humans. 3DBodyTex.Pose offers high quality and rich data containing 405 different real subjects in various clothing and poses, and 81k image samples with ground-truth 2D and 3D pose annotations. These images are generated from 200 viewpoints among which 70 challenging extreme viewpoints. This data was created starting from high resolution textured 3D body scans and by incorporating various realistic backgrounds. Retraining a state-of-the-art 3D pose estimation approach using data augmented with 3DBodyTex.Pose showed promising improvement in the overall performance, and a sensible decrease in the per joint position error when testing on challenging viewpoints. The 3DBodyTex.Pose is expected to offer the research community with new possibilities for generalizing 3D pose estimation from monocular in-the-wild images.