In the context of computer vision and human-robot interaction, forecasting 3D human poses is crucial for understanding human behavior and enhancing the predictive capabilities of intelligent systems. While existing methods have made significant progress, they often focus on predicting major body joints, overlooking fine-grained gestures and their interaction with objects. Human hand movements, particularly during object interactions, play a pivotal role and provide more precise expressions of human poses. This work fills this gap and introduces a novel paradigm: forecasting 3D whole-body human poses with a focus on grasping objects. This task involves predicting activities across all joints in the body and hands, encompassing the complexities of internal heterogeneity and external interactivity. To tackle these challenges, we also propose a novel approach: C3HOST, cross-context cross-modal consolidation for 3D whole-body pose forecasting, effectively handles the complexities of internal heterogeneity and external interactivity. C3HOST involves distinct steps, including the heterogeneous content encoding and alignment, and cross-modal feature learning and interaction. These enable us to predict activities across all body and hand joints, ensuring high-precision whole-body human pose prediction, even during object grasping. Extensive experiments on two benchmarks demonstrate that our model significantly enhances the accuracy of whole-body human motion prediction. The project page is available at https://sites.google.com/view/c3host.

The significance of telerobotic trajectory segmentation has been demonstrated on a range of skill training and robotic automation tasks. However, the trajectories of telerobot are characterized by complexity and high dimensionality, making it difficult to segment them accurately. Existing methods are often plagued by feature inefficiency, and the clustering methods are also difficult to properly model the trajectories. In addition, over-segmentation also affects the accuracy of the clustering-based segmentation methods. To address above problems, this article presents a new unsupervised approach that automatically segments multimodal trajectory data of a telerobot and solves the problem of over-segmentation through a postpromoting procedure. First, we present an unsupervised visual feature-extraction network, that is, a dense connection spatial convolution network (DCSC) to generate more discriminative features for clustering. The dense convolution and spatial convolution facilitates information flow, enhances feature propagation, and avoids manual annotation. Next, we develop an unsupervised trajectory segmentation method that is called multimodality clustering with Chinese restaurant process (MC-CRP). The model utilizes data from different modalities and segments the trajectory through hierarchical clustering. MC-CRP obtains more accurate results in a short period of time. To further improve the precision of trajectory segmentation, we merge over-segments based on predefined similarity measurements. Extensive experiments on the publicly available data set JIGSAWS show that the presented approach achieves 70.1% silhouette coefficient, 25.1% normalized mutual information, and 71.4% segmentation accuracy. These metrics demonstrate that the presented segmentation approach provides deeper insight into the trajectory features and improve the accuracy of segmentation more efficiently than others.

The detection of seismic small faults is vital in shale oil and gas exploration and development. Limited by the resolution of seismic exploration, it is difficult to effectively detect small faults. Recently, many fault characterization methods have been proposed. To overcome the obscurity of seismic resolution for small faults, time–frequency analysis algorithms and fault attributes have been employed to characterize small faults and stratigraphic inflection point. However, the traditional resolution of seismic time–frequency analysis algorithms greatly limits the accuracy of small fault identification. Therefore, there is a need to improve the resolution of seismic time–frequency analysis algorithms. Herein, we propose a new time–frequency analysis algorithm and workflow, high-order multichannel synchrosqueezing variational modal generalized S-transform (HMSVGST) based on variational mode decomposition and synchrosqueezing GST (SGST). The proposed algorithm differs from the original synchrosqueezing algorithm in that it decomposes and transforms the signal simultaneously, which preserves the original signal components and avoids interference between different signal components, thereby improving the time–frequency focusing ability. A high-order multichannel synchrosqueezing variational modal GST is employed to decompose the seismic data volume in the time–frequency domain, and the optimal surface voting technique is used to characterize small faults. We set the forward model with 5–30-m fault distance and the application of real seismic data; we show that the proposed method has a good ability to characterize small faults less than 10 m, which validated the proposed method.