remove motion artifacts

High-precision online estimation algorithms for robotics are highly sensitive to sensor timestamp accuracy; however, existing synchronization solutions struggle to simultaneously achieve real-time operation, low cost, and high temporal precision. To address this, we propose a real-time, trigger-based time synchronization system built on commodity hardware. Our approach employs a hardware-triggered mechanism to jointly schedule heterogeneous sensors operating at different frequencies, and integrates an enhanced clock synchronization protocol with nanosecond-resolution timestamping to ensure precise coordination between sensors and the onboard computer. Crucially, the system eliminates reliance on expensive dedicated timing hardware, thereby substantially mitigating the impact of timing errors on online estimation. Experimental evaluation on a physical robot platform demonstrates sub-microsecond synchronization accuracy, along with significant improvements in both estimation robustness and real-time performance.

This work addresses two key challenges in multimodal motion recognition: cross-dataset sensor format heterogeneity and time-consuming hyperparameter optimization. We propose the first end-to-end, fully automated, general-purpose framework for this task. The framework uniformly processes heterogeneous time-series sensor data, enabling fully automated preprocessing—including multimodal temporal alignment—model training, Optuna-based hyperparameter optimization, and standardized evaluation—without manual intervention. Its core innovations are zero-configuration cross-dataset transferability and plug-and-play edge deployment. Evaluated on ten diverse, heterogeneous datasets, the framework achieves state-of-the-art performance, improving average classification accuracy by 3.2% and accelerating end-to-end deployment throughput by 8× compared to conventional pipelines.

This work addresses the critical challenge of jointly designing sensor query rates and noise covariance under resource and cost constraints to meet prescribed trajectory estimation accuracy requirements. It presents the first formalization of this problem as a unified optimization model, leveraging semidefinite programming (SDP) within the Kalman filter error covariance framework to simultaneously optimize measurement scheduling and noise parameters. The proposed approach efficiently determines whether a given accuracy target is achievable and, when feasible, synthesizes a corresponding implementation strategy. Experimental validation demonstrates that the computed sensor configurations consistently attain the desired accuracy in both simulated and real-world scenarios, while also reliably identifying infeasible accuracy demands.

To address rare, transient, and precisely localized dynamic scientific phenomena—such as volcanic eruption plumes—this paper proposes an onboard real-time perception–decision–response closed-loop framework. Methodologically, it fuses forward-looking satellite imagery with lightweight CNNs and traditional machine learning models for edge-based plume detection, and integrates multi-objective trajectory planning to autonomously generate optimal high-resolution sensor pointing paths. Its key contribution lies in the first deep integration of event-driven real-time detection and online trajectory planning on an edge computing platform, enabling fully autonomous onboard observation scheduling. Simulation results demonstrate that, compared to baseline approaches, the framework achieves over a tenfold increase in scientific return, while total inference and planning latency remains below typical revisit intervals—substantially improving capture probability and data value for sparse dynamic events.

This work addresses the longstanding “last mile” problem in video-based motion capture, where reconstructed motions often exhibit physical implausibility and artifacts that necessitate extensive manual correction for industrial applications such as film and gaming. The authors propose a production-oriented physics-aware motion refinement framework that enhances both single- and multi-person motion sequences through physics-based optimization while seamlessly integrating keyframe editing to allow animators to inject stylistic adjustments. Developed in close collaboration with professional animators, the method balances automation efficiency with artist control, significantly improving the physical plausibility and visual quality of motion data. This approach markedly reduces post-processing effort and is designed to fit directly into real-world animation pipelines.

This study addresses the challenge of rapid change-point detection in high-dimensional multisensor systems under structural constraints and limited sensing resources. By integrating sparse modeling, heterogeneous data fusion, and a resource-adaptive sequential sampling strategy, the work extends classical change-point detection theory to large-scale, resource-constrained sensing scenarios and incorporates machine learning to handle cases with unknown system models. The proposed approach unifies sparse signal processing, multi-stream statistical decision-making, and resource-constrained optimization to enable simultaneous detection of multiple change points. This framework significantly enhances both applicability and scalability in high-dimensional, heterogeneous, and resource-limited environments while maintaining high detection efficiency.

This work addresses the lack of effective auditing mechanisms in current AI-based motion capture systems, which hinders verification of whether inferred skeletal poses conform to authentic human behavior. To tackle this challenge, the authors propose a novel auditing framework that integrates contextual reasoning with biomechanical symmetry principles. By embedding practical application contexts into the evaluation process and combining motion capture outputs with empirically verifiable real-world measurements—even in the absence of ground truth or amid contested annotations—the method enables rigorous empirical auditing of system behavior. The approach successfully uncovers implicit assumptions and biases in how ground truth is defined within existing systems, thereby offering both theoretical foundations and practical pathways for trustworthy assessment of motion capture technologies.

This work addresses the challenges of high early-stage uncertainty in manufacturing monitoring system development—leading to redundant modeling and substantial training costs—and the limited transferability of filtering pipelines in cross-domain image segmentation tasks. To tackle these issues, the authors propose a problem-centric design paradigm that constructs an abstract system model to continuously accumulate and retrieve historical segmentation tasks along with their associated filtering pipelines, enabling solution reuse and incremental optimization. The approach integrates similarity-based problem retrieval, abstract modeling, pipeline reuse, and a retrieval-augmented evolutionary learning mechanism. Experimental results demonstrate that the method significantly reduces training costs and late-stage revision risks, provides the first systematic validation of filtering pipeline transferability across similar segmentation tasks, and achieves a favorable balance among complexity, technical requirements, and reliability under lightweight model constraints.

This work addresses the challenge of controllably editing the motion trajectory of a target object in videos while preserving the original scene content. To this end, the authors propose a two-stage framework: first, a cross-view motion transformation module maps a user-specified trajectory—provided only in the initial frame—into per-frame bounding boxes that account for camera motion; second, a motion-conditioned video resynthesis module generates the object along this trajectory while maintaining background consistency. By eliminating the need for complex point-trajectory inputs, the method significantly enhances user-friendliness and temporal coherence. Experiments demonstrate that the approach produces more realistic, temporally consistent, and controllable motion edits on diverse real-world videos compared to existing image-to-video or video-to-video methods.