Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited uncertainty analysis have hindered progress in the field. To address this, we first develop an open-source library for human pose forecasting, including multiple models, supporting several datasets, and employing standardized evaluation metrics, with the aim of promoting research and moving toward a unified and consistent evaluation. Second, we devise two types of uncertainty in the problem to increase performance and convey better trust: 1) we propose a method for modeling aleatoric uncertainty by using uncertainty priors to inject knowledge about the pattern of uncertainty. This focuses the capacity of the model in the direction of more meaningful supervision while reducing the number of learned parameters and improving stability; 2) we introduce a novel approach for quantifying the epistemic uncertainty of any model through clustering and measuring the entropy of its assignments. Our experiments demonstrate up to $25\%$ improvements in forecasting at short horizons, with no loss on longer horizons on Human3.6M, AMSS, and 3DPW datasets, and better performance in uncertainty estimation. The code is available online at https://github.com/vita-epfl/UnPOSed.

翻译：近期，旨在解决根据历史观测序列预测未来三维人体姿态序列这一时空任务的方法呈军备竞赛式发展。然而，缺乏统一的基准测试及有限的不确定性分析阻碍了该领域的进展。为此，我们首先开发了一个开源人体姿态预测库，集成多种模型、支持多个数据集并采用标准化评估指标，旨在推动研究迈向统一且一致的评估体系。其次，我们在问题中设计了两种不确定性类型以提升性能并增强可信度：1）提出一种通过不确定性先验建立认知不确定性模型的方法，将不确定性模式知识注入模型，使模型能力聚焦于更有意义的监督方向，同时减少学习参数数量并提升稳定性；2）引入一种通过聚类与分配熵量化任意模型认知不确定性的新型方法。实验表明，在Human3.6M、AMSS和3DPW数据集上，我们的方法在短时域预测中提升达25%，长时域预测无性能损失，且不确定性估计表现更优。代码已开源：https://github.com/vita-epfl/UnPOSed。