不確実性を考慮した信頼性の高い人間姿勢予測
Toward Reliable Human Pose Forecasting with Uncertainty
人間の姿勢予測のための統一ベンチマークとライブラリを構築し、偶然的不確実性と認識的不確実性をモデル化することで予測性能と信頼性を向上させた研究。
著者: Saeed Saadatnejad, Mehrshad Mirmohammadi, Matin Daghyani, Parham Saremi, Yashar Zoroofchi Benisi, Amirhossein Alimohammadi, Zahra Tehraninasab, Taylor Mordan, Alexandre Alahi
分類: cs.CV, cs.HC, cs.RO
原文アブストラクト
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.
