arXiv Machine Learning

EVFormer: An Egocentric Vision-EMG Bidirectional Attention Model for Bimanual Hand Pose Estimation

arXiv AI

Jul 28

Multimodal Surface EMG Hand Gesture Recognition Using Query-Based Transformers for Prosthetic Control

arXiv:2607. 22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control.

By Federico Del Pup, Elisa Tentori, Manfredo Atzori

llms multimodal benchmarks

arXiv Machine Learning

Oct 2

EFormer: Temporally Aligned Local Correction for Continuous sEMG-Based Hand Pose Tracking

arXiv:2609.38932v1 Announce Type: new Abstract: Surface electromyography (sEMG) provides a wearable, camera-free signal for continuous hand-motion inference. Mapping muscle activity to joint kinemati...

By JiaCheng Ge, SiYu Zhang

rag

arXiv Computer Vision

Oct 2

EgoFound3R: End-to-End Egocentric Hand Reconstruction in World Space with Point-Wise Interaction Attributes

arXiv:2610.01210v1 Announce Type: new Abstract: Egocentric video has become a primary source of supervision for embodied models, and its value rests on recovering hand motion in world coordinates, wh...

By Hongming Fu, Jingcheng Shi, Wenjia Wang, Binhua Zuo, Bo Zhao

robotics efficiency

arXiv Computer Vision

Sep 16

EventEgoHands++: Event-based Egocentric 3D Hand Mesh Reconstruction with Real Dataset

EventEgoHands++ is a new framework for reconstructing 3D hand meshes from egocentric event-based cameras. It introduces a Hand Detector that provides instance-level bounding boxes and masks for left and right hands, and an Adaptive Attention module that uses these detections to model spatial relationships and interactions. The authors extend the synthetic N-HOT3D dataset and create EEH‑R, a large real-world event-based egocentric hand dataset with about 1 million annotated frames, and show that their method outperforms existing baselines on both synthetic and real data.

By Ryosei Hara, Wataru Ikeda, Masashi Hatano, Mariko Isogawa

computer-vision robotics

arXiv Machine Learning

Jul 7

KinEMbed: Decoding Kinematics from Electromyography via Cross-Modal Contrastive Learning

arXiv:2607. 04820v1 Announce Type: new Abstract: Decoding hand kinematics from surface electromyography (EMG) is a core challenge in wearable biosignal processing with clinical relevance for prosthetic control and motor rehabilitation.

By Sofia Gilardini, Chenfei Ma, Kianoush Nazarpour

rag multimodal

arXiv Computer Vision

Sep 22

BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion

arXiv:2609.23352v1 Announce Type: new Abstract: Bimanual interaction produces complementary tactile views of the same physical process, yet existing tactile representation learning largely models the...

By Chenxin Liang, Youchen Lai, Chuqiao Lyu, Tianxing Chen, Shoujie Li, Wenbo Ding

arXiv Computer Vision

Sep 22

Estimating Accurate Hand Pose in Camera Space with Vision Transformer

arXiv:2609.24424v1 Announce Type: new Abstract: Monocular RGB-based hand pose estimation has emerged as a critical research frontier in computer vision. The local hand pose estimation methods predict...

By Kaiwen Ren, Yiran Jiang, Yongjing Ye, Shihong Xia

llms rag computer-vision benchmarks

arXiv Computer Vision

Aug 21

HandMvNet: Real-Time 3D Hand Pose Estimation Using Multi-View Cross-Attention Fusion

arXiv:2608. 20093v1 Announce Type: new Abstract: In this work, we present HandMvNet, one of the first real-time method designed to estimate 3D hand motion and shape from multi-view camera images.

By Muhammad Asad Ali, Nadia Robertini, Didier Stricker

computer-vision benchmarks

arXiv Machine Learning

Jun 29

Cross-view Multimodal Vision-Based Assessment Framework for Traditional Chinese Medicine Rehabilitation Training

arXiv:2606. 28104v1 Announce Type: cross Abstract: Vision-based assessment can provide convenient and cost-effective evaluation in Traditional Chinese Medicine (TCM) rehabilitation training, where action quality assessment (AQA) from computer vision offers a promising solution.

By Francis Xiatian Zhang, Hao Yao, Shengxuan Chen, Hong Zhu, Hongxiao Jia, Sisi Zheng, Hubert P. H. Shum

computer-vision robotics multimodal

arXiv Computer Vision

Sep 16

MEgoVista: Multi-view Ego-aware Motion Estimation for Metric 4D Hands and Head in the Wild

MEgoVista is an offline pipeline that converts a single unprepared egocentric video into metric two‑hand and head motion within a gravity‑aligned world frame. It uniquely reconstructs motion in environments beyond studio volumes, uses calibrated stereo for absolute scale, and evaluates its outputs against independent optical capture to audit accuracy. The system thus expands the settings where high‑fidelity hand‑motion labels can be generated from natural, head‑worn recordings.

By Jiangong Xiao (Northwestern Polytechnical University), Zhihao Zhang (Xi'an Jiaotong University), Yifei Dong (Maniformer), Chao Ma (Maniformer), Zhouyi Jin (Maniformer), Zhiwen Hou (Maniformer), Li Liu (Maniformer), Weihuang Chen (Xi'an Jiaotong University), Hongbin Sun (Xi'an Jiaotong University), Maoqing Yao (Maniformer)

robotics

arXiv Machine Learning

Sep 23

EMGBlend: Heterogeneity-Aware Self-Supervised Pretraining for Gesture and Force Decoding

arXiv:2609.25582v1 Announce Type: new Abstract: Public surface electromyography (EMG) datasets vary widely in electrode layout, channel count, frequency support, and size. Simply mixing them for pret...

By Yuwei Jia, Cheng Zhong, Jinyang Yu, Zhe Cui

arXiv Machine Learning

Jul 31

Recognition and Label-Free Adaptation Across Recording Sessions in Surface-EMG Gesture Decoding

arXiv:2607. 27568v1 Announce Type: new Abstract: Recognition accuracy obtained during a recording session does not persist when a user puts on the electrodes again after the electrodes had previously been removed.

By Jethro Odeyemi, W. J. Zhang

safety

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