Dual-mode COFs@CNTs-based hydrogel: real-time motion monitoring and efficient solar desalination.

Exotic continental gastropods and emerging helminth Parasites: Insights from the Iberian Peninsula.

Indirect measurement and recognition of human finger motion via flexible piezoelectric POSS-doped poly(L-lactic acid) sensor

We have recently seen some progress in the current field of human-human interaction generation. However, directly generating complex two-person interactive motions remains a significant challenge. Meanwhile, these models typically employ two independent timelines when generating motions for interactive scenarios involving two individuals. This design overlooks the temporal dependencies between motions at each timestep and fails to account for the roles of active and reactive participants during the generation process, often resulting in unrealistic and unnatural motions. In this work, we propose HiTMM, a novel framework for Human interaction generation based on Temporal Masked Modeling. HiTMM first decomposes the human interaction into two separate single-person motions. Individual motions within the interaction belong to the same type, enabling them to be mapped to a shared latent space through a coarse-to-fine approach that produces multi-layer discrete tokens. We then arrange all tokens of the two interacting individuals along a shared timeline. Subsequently, we employ a masked transformer and a residual transformer to model the base-layer and rest-layer motion tokens. Both the base-layer and rest-layer motion tokens are arranged along a single timeline, allowing the model to explicitly capture the temporal order and initiating role embedded in the sequence, where the first individual's motion initiates the interaction. Note that, our model utilizes a shared temporal representation, making it capable of performing temporal editing on specific regions within human interaction sequences. Experimental results show that our model achieves an FID of 5.017 on the InterHuman dataset, surpassing the current state-of-the-art model (vs 5.154 for InterMask), and an FID of 0.373 on the InterX dataset (vs 0.399 for InterMask).

Baseline dynamics in human visual behaviour.

Authoring high-quality character animation is essential in multimedia production, especially for pre-rendered formats such as films and TV series. These workflows are often iterative, requiring frequent adjustments and immediate visual feedback, and typically involve editing existing motion, such as mocap data. We present Neural Motion Path (NMP), a deep learning-based system designed to support this authoring process by enabling full-body motion editing through joint-level motion path manipulation. While joint rotations are essential for expressive human motion, explicitly specifying them imposes a significant burden on users; NMP addresses this challenge by enabling intuitive position-only motion path editing while implicitly inferring plausible rotation trajectories. To address the inherent tension between motion plausibility and precise constraint satisfaction, NMP explicitly decouples motion synthesis from constraint enforcement within an autoregressive framework. NMP generates realistic, context-aware motion without fine-tuning every path detail, making it accessible to novices while supporting the demands of detailed animation authoring. It combines a motion generator with a novel RotationNet for inferring joint rotation, and a constraint imposer that enforces end-effector constraints via an Explicit-Weight Sparse Expert Model (EW-SEM). The system supports terrain adaptation and authoring operations like Concatenate, Insert, and Mix. Implemented as a Blender add-on, NMP supports real-time playback and interactive workflows. A user study with novice users shows that NMP improves satisfaction, efficiency, and perceived motion quality compared to the conventional layered keyframing approach, highlighting its potential as an accessible and effective authoring tool.

LiDAR-based human motion capture holds great promise for large-scale, unconstrained environments. However, existing approaches often rely on clean, pre-segmented point clouds and struggle with noisy or dynamic scenes, limiting their practical applicability. We propose OptimalCap, a robust and efficient LiDAR-based framework that integrates hierarchical skeletal modeling and kinematic-aware temporal optimization to enable accurate, coherent, and real-time multi-human motion capture. To support training and evaluation under realistic disturbances, we also introduce NoiseMotion, a large-scale synthetic dataset simulating human-object interactions in noisy environments. Extensive experiments on public and synthetic benchmarks demonstrate that OptimalCap achieves state-of-the-art accuracy, robustness, and temporal consistency, while supporting over 20 individuals, at 60 FPS and up to 100 meters, setting a new standard for scalable, real-world LiDAR-based motion capture.

The generation of anchor-style product promotion videos presents promising opportunities in e-commerce, advertising, and consumer engagement. Despite advancements in pose-guided human video generation, creating product promotion videos remains challenging. In addressing this challenge, we identify the integration of human-object interactions (HOI) into pose-guided human video generation as a core issue. To this end, we introduce AnchorCrafter, a novel diffusion-based system designed to generate 2D videos featuring a target human and a customized object, achieving high visual fidelity and controllable interactions. Specifically, we propose two key innovations: the HOI-appearance perception, which enhances object appearance recognition from arbitrary multi-view perspectives and disentangles object and human appearance, and the HOI-motion injection, which enables complex human-object interactions by overcoming challenges in object trajectory conditioning and inter-occlusion management. Extensive experiments show that our system improves object appearance preservation by 7.5%, and achieves the best video quality compared to existing state-of-the-art approaches. It also outperforms existing approaches in maintaining human motion consistency and high-quality video generation.

Continuous prediction of knee and ankle joint angles based on sEMG and hip joint angle fusion signals by NRBO-LSTM network

Improving long-term human motion prediction for physical exercise actions using a lightweight approach

Lagrange-anchored implicit pose fields: A physics-consistent framework for human motion pose estimation

Despite substantial progress in decoding biosignals for human motion prediction, the influence of participant- and experiment-related factors on the decodability of these signals has received comparatively little attention. This study evaluates the continuous prediction of hand and wrist joint flexion using the MyoKi database, which comprises surface electromyography, inertial measurement units, and force myography data from 35 participants without disabilities performing 74 daily-life tasks. Unlike existing datasets, MyoKi includes tasks that mimic real-world scenarios by allowing natural movement variations and muscle fatigue. Using a long short-term memory neural network, the impact of participant- and experiment-related factors on decoding accuracy was investigated. Our results show that both expanding sensor coverage to additional muscle regions and combining multiple sensor modalities significantly improve decoding performance, with the greatest gains observed for joints controlled by extrinsic muscles. Muscle fatigue, recording time, and participant characteristics such as weight also influenced model accuracy. However, decoding of movements driven by intrinsic hand muscles remains challenging due to anatomical limitations. These findings highlight the importance of sensor placement and multimodal fusion for myoelectric decoding and provide guidance for optimizing sensor configurations in future prosthetic and robotic applications.

Advances in conductive supramolecular hydrogels for applications in wearable electronics.

MXene-doped hydrogels for stretch-insensitive low-pass filters and human motion monitoring

High-performance pressure sensors based on graphene fibers with bilateral dense structures for human motion monitoring

Balancing global coherence and hand-level detail: Frequency-decomposed whole-body human motion prediction

High-strength microneedle sensor for sensitive lactic acid and pH detection.

Validation, characterization, and utility of markerless motion capture in a large cohort of pediatric patients with complex gait patterns.

Temperature-stimulus-responsive hydrogels with outstanding mechanical and electrical properties have attracted extensive attention in the field of health detection and information encryption due to their unique environmental responsiveness. In this work, a flexible, conductive, tunable thermoresponsive, and antifreezing poly(acrylic acid-acrylamide)/gelatin/lithium chloride (P(AA-AM)/Gelatin/LiCl, PGL) hydrogel was fabricated via a simple one-pot approach. Functional regulator LiCl effectively enhances the mechanical properties of the PGL hydrogel through salting-out effect and phase separation. Also, the transparent-to-opaque transition is reversible and repeatable, and the phase transition temperature can be sensitively adjusted by LiCl mass in the PGL hydrogels. Especially, the PGL0.8 hydrogel with a strength of 180 kPa, a strain of 620%, and a conductivity of 2.16 S m-1 can serve as the strain sensor (gauge factor of 1.95) and temperature sensor (temperature resistance coefficient of 2.05%/°C at 25-45 °C). Benefiting from its excellent performance, the as-prepared PGL hydrogels can capture real-time electrical signals, including human joint motions, subtle physiological movements, and temperature fluctuations, when utilized as wearable multifunctional sensors. Therefore, the tunable thermoresponsive and antifreezing hydrogel holds promise for human health monitoring and information encryption, meeting theoretical and practical application requirements of flexible wearable sensors.

Synergistic near-infrared photothermal and electrical stimulation of MgMOF/black phosphorus-reinforced conductive organohydrogels for integrated diagnosis-therapy-repair of complex wounds.