arXiv:2610.32517v1 Announce Type: new
Abstract: Hand landmarks serve as input for gesture recognition, sign language understanding, and touchless human-computer interaction. Yet hand pose estimators...
By Gibran Benitez-Garcia, Hiroki Takahashi
The paper argues that in structured image classification, the key question is what information a classifier should receive before making a prediction, rather than which algorithm performs best. It proposes a systematic framework for constructing landmark-derived representations—such as coordinate, distance, angle, and hybrid features—and evaluates them on static hand gesture recognition. Experiments show that hybrid representations, which combine complementary geometric components, outperform raw coordinate features and other single-type representations, highlighting the importance of thoughtful feature construction.
By Saravana Mauree, Sakshi Arya
arXiv:2610.33130v1 Announce Type: new
Abstract: Human activity recognition benchmarks underrepresent culturally situated forms of movement and interaction. We introduce Ampe-DB, a multimodal dataset...
By Ruth Senior Biney, Isaac Kojo Yedonu Aboah
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
arXiv:2608. 10588v1 Announce Type: cross Abstract: Purpose: Fine-grained handshape recognition supports computational sign-language transcription, recognition, and translation, but broad, phonetically defined visual inventories with signer-aware evaluation remain limited.
By Ushnish Sarkar, Suvajit Patra, Bhaswar Chattopadhyay, Pranab Singha Roy, Tapas Samanta
Driven by the availability of large-scale datasets, Human Pose Estimation (HPE) plays a critical role in numerous downstream tasks. However, mainstream benchmarks exhibit severe representation bias, predominantly featuring able-bodied individuals.