Evaluation of Pose Estimation Systems for Sign Language Translation

TL;DR

This paper systematically compares various pose estimation systems for sign language translation, analyzing their impact on translation quality, robustness, and stability, and provides insights into the best-performing models.

Contribution

It offers a comprehensive evaluation of pose estimators for SLT, highlighting the importance of specific models like Sapiens and SDPose for improved translation accuracy.

Findings

SDPose and Sapiens outperform MediaPipe in BLEU scores (~11.5 vs. 10).

Sapiens correctly handles all occlusion cases tested, unlike OpenPifPaf.

Estimators missing hand keypoints correlate with lower translation quality.

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 implementation detail, with systems defaulting to widely available tools such as MediaPipe Holistic or OpenPose. We present a systematic comparison of pose estimators for pose-based SLT, covering widely used baselines (MediaPipe Holistic, OpenPose) and newer whole-body/high-capacity models (MMPose WholeBody, OpenPifPaf, AlphaPose, SDPose, Sapiens, SMPLest-X). We quantify downstream impact by training a controlled SLT pipeline on RWTH-PHOENIX-Weather 2014 where only the pose representation varies, evaluating with BLEU and BLEURT. To contextualize translation outcomes, we analyze temporal stability, missing hand keypoints, and robustness to occlusion using higher-resolution videos from the Signsuisse dataset. SDPose and Sapiens achieve the best translation performance (BLEU ~11.5), outperforming the common MediaPipe baseline (BLEU ~10). In occlusion cases, Sapiens is correct in all tested instances (15/15), while OpenPifPaf fails in nearly all (1/15) and also yields the weakest translation scores. Estimators that frequently leave out hand keypoints are associated with lower BLEU/BLEURT. We release code that can be used not only to reproduce our experiments, but also considerably lowers the barrier for other researchers to use alternative pose estimators.