arXiv AI
HandAnthro: Automated Hand Anthropometry from a Single Image
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arXiv AI
Jun 2
Train, Test, Re-evaluate: Schedule-Sensitive Evaluation of Generative Data for Hand Detection
arXiv:2606. 01896v1 Announce Type: cross Abstract: Generated (or synthetic) image data is increasingly used to augment or replace real training datasets when target imagery is scarce, expensive, or biased.
arXiv Computer Vision
Aug 21
ScaleHP: Scale-Mediated Optimization of Coupled Errors for Metric-Space Hand Pose Estimation
arXiv:2606. 25619v2 Announce Type: replace Abstract: In this paper, we present ScaleHP, a unified framework that explicitly represents per-instance metric scale to resolve the coupled errors in calibrated camera-space hand pose estimation.
Hugging Face Trending Papers
Aug 23
Vision-Language Models for Occupational Physical Exposure Assessment: Estimating External Hand Forces in Manual Material Handling Tasks from RGB Video
External hand forces are important inputs to biomechanical analyses of occupational physical exposure and injury risk, yet continuous force measurements during manual material handling (MMH) typically...
arXiv AI
Aug 25
Vision-Language Models for Occupational Physical Exposure Assessment: Estimating External Hand Forces in Manual Material Handling Tasks from RGB Video
The study presents a vision‑language model pipeline that estimates dynamic, triaxial, bilateral external hand forces during manual material handling tasks using only RGB video and known box mass. By combining text‑guided ROI localization, pretrained vision‑transformer features, and transformer‑based temporal regression, the model achieved root mean square errors of about 4.7–5.6 N for horizontal and mediolateral forces and 10.6–11.0 N for vertical forces across various camera setups. The approach demonstrated that including the handled object as a second ROI and using multi‑camera capture improved peak‑force estimation, showing the feasibility of sensor‑free force estimation for occupational exposure assessment.
arXiv Computer Vision
Sep 3
Video-Based Palm-Vein Authentication under Challenging Conditions
The paper introduces the Columbia University Palm‑vein (CUP) dataset, the first public video‑based palm‑vein dataset that captures palms under four surface conditions—clean, warm, wet, and dirty—along with physiological and demographic metadata. Twenty‑one recognizers are benchmarked on CUP, revealing that models performing well on clean palms lose most accuracy on dirty palms, with mean EER roughly quadrupling. The authors propose a lightweight design that fuses global cosine similarity with a saliency‑steered region‑level optimal transport, achieving state‑of‑the‑art performance across all surfaces while reducing parameters and computational cost, and they identify demographic gaps in warm‑condition performance.
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...