Manual lifting tasks are a major contributor to work-related musculoskeletal disorders, and effective ergonomic risk assessment is essential for quantifying physical exposure and informing ergonomic interventions. The Revised NIOSH Lifting Equation (RNLE) is a widely used ergonomic risk assessment tool for lifting tasks that relies on six task variables, including horizontal (H) and vertical (V) hand distances; such distances are typically obtained through manual measurement or specialized sensing systems and are difficult to use in real-world environments. We evaluated the feasibility of using innovative vision-language models (VLMs) to non-invasively estimate H and V from RGB video streams. Two multi-stage VLM-based pipelines were developed: a text-guided detection-only pipeline and a detection-plus-segmentation pipeline. Both pipelines used text-guided localization of task-relevant regions of interest, visual feature extraction from those regions, and transformer-based temporal regression to estimate H and V at the start and end of a lift. For a range of lifting tasks, estimation performance was evaluated using leave-one-subject-out validation across the two pipelines and seven camera view conditions. Results varied significantly across pipelines and camera view conditions, with the segmentation-based, multi-view pipeline consistently yielding the smallest errors, achieving mean absolute errors of approximately 6-8 cm when estimating H and 5-8 cm when estimating V. Across pipelines and camera view configurations, pixel-level segmentation reduced estimation error by approximately 20-30% for H and 35-40% for V relative to the detection-only pipeline. These findings support the feasibility of VLM-based pipelines for video-based estimation of RNLE distance parameters.
University of Washington（华盛顿大学）；Indian Institute of Technology Gandhinagar（印度理工学院甘地纳加尔分校）
Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications. We present a foundational paradigm to address this challenge by formulating the problem as one of action segmentation from RGB-D camera videos. Spatial features are first learned using a deep convolutional model from the video frames, which are then fed sequentially to temporal convolutional networks to semantically segment the frames into a hierarchy of actions, which are either ergonom…
Istituto Italiano di Tecnologia（意大利理工学院）；University of Genova（热那亚大学）
This paper presents a novel 3D human pose estimation approach using a single stream of asynchronous events as input. Most of the state-of-the-art approaches solve this task with RGB cameras, however struggling when subjects are moving fast. On the other hand, event-based 3D pose estimation benefits from the advantages of event-cameras, especially their efficiency and robustness to appearance changes. Yet, finding human poses in asynchronous events is in general more challenging than standard RGB pose estimation, since little or no events are triggered in static scenes. Here we propose the firs…
Kwangwoon University（光云大学）；Seoul National University（首尔国立大学）
This study presents a new network (i.e., PoseLifter) that can lift a 2D human pose to an absolute 3D pose in a camera coordinate system. The proposed network estimates the absolute 3D location of a target subject and generates an improved 3D relative pose estimation compared with existing pose-lifting methods. Using the PoseLifter with a 2D pose estimator in a cascade fashion can estimate a 3D human pose from a single RGB image. In this case, we empirically prove that using realistic 2D poses synthesized with the real error distribution of 2D body joints considerably improves the performance o…
University College London（伦敦大学学院）；The Turing Institute（图灵研究所）；The University of Edinburgh（爱丁堡大学）
We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations. The entire process is trained end-to-end, is extremely efficient and obtains state- of-the-art results on Human3.6M outperforming previous approaches both on 2D and 3D errors.
UniPose: Unified Cross-modality Pose Prior Propagation towards RGB-D data for Weakly Supervised 3D Human Pose Estimation
National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（多谱段信息智能处理技术国家重点实验室，人工智能与自动化学院，华中科技大学）
WildLIFT: Lifting monocular drone video to 3D for species-agnostic wildlife monitoring
Fondazione Bruno Kessler（布雷诺·科塞勒基金会）；University of Muenster（明斯特大学）；Max Planck Institute of Animal Behaviour（动物行为马克斯·普朗克研究所）；University of Konstanz（康斯坦茨大学）
Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging
University of Innsbruck（因斯布鲁克大学）；VASCage-Research Centre on Vascular Ageing and Stroke（VASCage血管老化与中风研究中心）；MRC Laboratory of Molecular Biology（英国医学研究委员会分子生物学实验室）；Medical University of Innsbruck（因斯布鲁克医科大学）
PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM
The University of Sydney（悉尼大学）；The University of Hong Kong（香港大学）；ShanghaiTech University（上海科技大学）；The University of Melbourne（墨尔本大学）；Texas A&M University（德克萨斯农工大学）
Vision-Based Human Awareness Estimation for Enhanced Safety and Efficiency of AMRs in Industrial Warehouses
Fraunhofer Austria Research GmbH（弗劳恩霍夫奥地利研究有限公司）；Computer Vision Lab（计算机视觉实验室）；Digital Factory Vorarlberg GmbH（数字工厂沃尔夫斯堡有限公司）；Institute of Computer Technology（计算机技术研究所）
