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
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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.