Scriboora: Rethinking Human Pose Forecasting

• URL: http://arxiv.org/abs/2511.15565v1
• Date: Wed, 19 Nov 2025 15:58:33 GMT
• Title: Scriboora: Rethinking Human Pose Forecasting
• Authors: Daniel Bermuth, Alexander Poeppel, Wolfgang Reif,
• Abstract summary: This paper evaluates a wide range of pose forecasting algorithms in the task of absolute pose forecasting.<n>Recent speech models can be efficiently adapted to the task of pose forecasting, and improve current state-of-the-art performance.
• Score: 44.79834103607383
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Human pose forecasting predicts future poses based on past observations, and has many significant applications in areas such as action recognition, autonomous driving or human-robot interaction. This paper evaluates a wide range of pose forecasting algorithms in the task of absolute pose forecasting, revealing many reproducibility issues, and provides a unified training and evaluation pipeline. After drawing a high-level analogy to the task of speech understanding, it is shown that recent speech models can be efficiently adapted to the task of pose forecasting, and improve current state-of-the-art performance. At last the robustness of the models is evaluated, using noisy joint coordinates obtained from a pose estimator model, to reflect a realistic type of noise, which is more close to real-world applications. For this a new dataset variation is introduced, and it is shown that estimated poses result in a substantial performance degradation, and how much of it can be recovered again by unsupervised finetuning.

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