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.

翻译：许多手语翻译（SLT）系统基于姿态序列而非原始视频进行处理，以降低输入维度、提升可移植性并部分实现手语者的匿名化。姿态估计器的选择常被视为实现细节，系统默认采用MediaPipe Holistic或OpenPose等广泛可用的工具。我们针对基于姿态的手语翻译任务，对姿态估计器进行了系统性对比，涵盖广泛使用的基线方法（MediaPipe Holistic、OpenPose）以及新一代全身/高容量模型（MMPose WholeBody、OpenPifPaf、AlphaPose、SDPose、Sapiens、SMPLest-X）。通过在RWTH-PHOENIX-Weather 2014数据集上训练受控的SLT流水线（仅改变姿态表示方式），并利用BLEU和BLEURT指标评估下游影响。为解释翻译结果，我们利用Signsuisse数据集的高分辨率视频，分析了时间稳定性、缺失手部关键点以及对遮挡的鲁棒性。SDPose和Sapiens取得最佳翻译性能（BLEU约11.5），优于常见的MediaPipe基线（BLEU约10）。在遮挡场景中，Sapiens在所有测试实例中均表现正确（15/15），而OpenPifPaf几乎全部失败（1/15），且其翻译得分最低。频繁缺失手部关键点的估计器与较低的BLEU/BLEURT值相关。我们公开了代码，既可复现实验，也显著降低了其他研究者使用替代姿态估计器的门槛。