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New datasets boost AI hand detection and pose estimation

By PulseAugur Editorial · [3 sources] ·

Researchers have developed new datasets to improve hand detection and pose estimation, addressing limitations in existing real-world data. One dataset, synthesized from the Egohands dataset, uses event-based and RGB cameras to overcome motion blur and low frame rates. Another dataset, AnyHand, provides a large-scale collection of synthetic RGB-D images with detailed annotations for 3D hand pose estimation, including occlusions and hand-object interactions. AI

IMPACT These datasets aim to improve the accuracy and robustness of AI models for hand-related tasks, potentially enabling more sophisticated human-robot interaction and augmented reality applications.

RANK_REASON The cluster contains two academic papers introducing new datasets for computer vision tasks.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New datasets boost AI hand detection and pose estimation

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COVERAGE [3]

  1. arXiv cs.CV TIER_1 English(EN) · Bharghav Kota (Zurich University of Applied Sciences, W\"adenswil, Switzerland), Yulia Sandamirskaya (Zurich University of Applied Sciences, W\"adenswil, Switzerland) ·

    A Multimodal RGB and Events Dataset for Hand Detection in First-Person View

    arXiv:2606.10790v1 Announce Type: new Abstract: Existing hand detection algorithms work on images and the detection rate is restricted by the frame rate of the camera. In hand detection applications for moving robotic systems, conventional cameras cause motion blur, especially in…

  2. arXiv cs.CV TIER_1 English(EN) · Yulia Sandamirskaya ·

    A Multimodal RGB and Events Dataset for Hand Detection in First-Person View

    Existing hand detection algorithms work on images and the detection rate is restricted by the frame rate of the camera. In hand detection applications for moving robotic systems, conventional cameras cause motion blur, especially in darker lighting conditions. We can leverage the…

  3. arXiv cs.CV TIER_1 English(EN) · Chen Si, Yulin Liu, Bo Ai, Jianwen Xie, Rolandos Alexandros Potamias, Chuanxia Zheng, Hao Su ·

    AnyHand: A Large-Scale Synthetic Dataset for RGB(-D) Hand Pose Estimation

    arXiv:2603.25726v3 Announce Type: replace Abstract: We present AnyHand, a large-scale synthetic dataset designed to advance the state of the art in 3D hand pose estimation. While recent works with foundation approaches have shown that scaling training data markedly improves hand …

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