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ScaleHP framework estimates hand pose in metric space

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ScaleHP, a novel framework for estimating hand pose in metric space, addressing a limitation in existing methods that predict poses relative to a root coordinate system. ScaleHP leverages the intrinsic proportional relationships of human hand bones as anthropometric priors to infer metric scale. The system utilizes a transformer-based decoder with a dedicated scale token to fuse multi-scale features and achieves state-of-the-art performance on benchmarks like FreiHand, DexYCB, and HO3Dv3. AI

IMPACT Improves accuracy for human-computer interaction and robotics by enabling metric-scale hand pose estimation.

RANK_REASON Academic paper detailing a new method for computer vision. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ScaleHP framework estimates hand pose in metric space

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ruitao Jing, Xingyu Chen, Hongyang Li, Qing Jiang, Yukai Shi, Lei Zhang ·

    ScaleHP: Estimating Hand Pose in Metric Space

    arXiv:2606.25619v1 Announce Type: new Abstract: Accurate metric-space hand pose estimation (HPE) is essential for immersive human-computer interaction and robotics. However, most existing methods predict poses in a root-relative coordinate system and cannot estimate the hand in a…

  2. arXiv cs.CV TIER_1 English(EN) · Lei Zhang ·

    ScaleHP: Estimating Hand Pose in Metric Space

    Accurate metric-space hand pose estimation (HPE) is essential for immersive human-computer interaction and robotics. However, most existing methods predict poses in a root-relative coordinate system and cannot estimate the hand in absolute metric scale. In this work, we observe t…

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