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
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
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
arXiv:2606.19156v2 Announce Type: replace
Abstract: Dynamic 3D hand reconstruction from egocentric videos is essential for next-generation computing platforms such as AR/VR and AI glasses. Despite it...
By Jeongmin Bae, Seoha Kim, Marc Pollefeys, Mahdi Rad, Youngjung Uh, Taein Kwon
arXiv:2609.24482v1 Announce Type: new
Abstract: Monocular 3D human pose estimation (HPE) remains challenging due to depth ambiguity, occlu- sions, and the need for temporal consistency. While multi-v...
By Mena Kamel, Natalie Won, Amrut Sarangi, Sven Jager, Albert Pla Planas
KAD-Net introduces a Kinematics-Aware Decoupled Learning Network for 3D hand pose estimation from a single depth image. It employs a Finger Topology Constraint module that uses local kinematic representations of three consecutive finger joints to better model distal joint relationships and handle occlusion. The architecture also decouples 2D joint localization from depth estimation in a hierarchical multitask framework, reducing feature interference and improving accuracy on benchmark datasets such as ICVL, NYU, and MSRA.
By Jun Lu, Zhenming Chen, Lin Chen, Kanlun Tan, Xiaoling Li, Qiao Liu
Accurate monocular 4D hand reconstruction remains challenging. Per-frame discriminative regressors lack temporal context and often produce jittery predictions.
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
arXiv:2608.30521v1 Announce Type: new
Abstract: Conventional algebraic triangulation solves 3D human pose estimation (HPE) from multi-view 2D keypoints. The typical approach, decoding 2D keypoints fr...
By Ziliang Xiong, Henglin Shi, Per-Erik Forssen
arXiv:2605.20992v4 Announce Type: replace
Abstract: We ask whether everyday open-world monocular videos can be turned into reusable 4D interaction primitives: articulated hand motion, object shape wi...
By Hao Xu, Yilin Liu, Yinqiao Wang, Chi-Wing Fu, Niloy J. Mitra
arXiv:2607. 11221v1 Announce Type: cross Abstract: Accurate monocular 4D hand reconstruction remains challenging.
By Mingxi Xu, Bowen Duan, Yi Gu, Zhengyang Shen, Renjing Xu, Yutao Yue
arXiv:2608.20308v2 Announce Type: replace
Abstract: Egocentric video offers scalable manipulation data for embodied AI, yet recovering metric 3D hand trajectories remains challenging due to severe ob...
By Yufei Liu, Xixi Wang, Hao Li, Ganlong Zhao, Kaitong Cai, Chengkai Jin, Chunxiao Liu, Jianbo Liu, Siyuan Huang, Xingang Pan, Hongsheng Li