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New study compares pose estimators for sign language translation systems

By PulseAugur Editorial · [2 sources] ·

A new paper evaluates various pose estimation systems for their effectiveness in sign language translation (SLT). Researchers compared common tools like MediaPipe Holistic and OpenPose against newer models such as SDPose and Sapiens. The study found that SDPose and Sapiens achieved the highest translation performance, outperforming the widely used MediaPipe baseline, and demonstrated better robustness in occlusion scenarios. The findings suggest that the choice of pose estimator significantly impacts SLT accuracy, particularly concerning the handling of hand keypoints and temporal stability. AI

IMPACT Improves SLT system development by guiding the selection of optimal pose estimation models for better accuracy and robustness.

RANK_REASON This is a research paper evaluating existing systems and proposing a new comparison methodology.

Read on arXiv cs.CL →

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

New study compares pose estimators for sign language translation systems

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Catherine O'Brien, Gerard Sant, Mathias M\"uller, Sarah Ebling ·

    Evaluation of Pose Estimation Systems for Sign Language Translation

    arXiv:2604.24609v1 Announce Type: new Abstract: Many sign language translation (SLT) systems operate on pose sequences instead of raw video to reduce input dimensionality, improve portability, and partially anonymize signers. The choice of pose estimator is often treated as an im…

  2. arXiv cs.CL TIER_1 English(EN) · Sarah Ebling ·

    Evaluation of Pose Estimation Systems for Sign Language Translation

    Many sign language translation (SLT) systems operate on pose sequences instead of raw video to reduce input dimensionality, improve portability, and partially anonymize signers. The choice of pose estimator is often treated as an implementation detail, with systems defaulting to …

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