用可逆网络建模人体动作不确定性，提升机器人协作安全性。
Uncertainty-aware Probabilistic 3D Human Motion Forecasting via Invertible Networks
- 引入可逆网络在解耦潜空间中参数化动作姿态
- 显式预测未来潜变量分布，实现精准不确定性量化
- 适合需要风险感知的智能机器人与人机协作场景
3D人体动作预测旨在支持自动驾驶等应用。在人机协作等安全关键场景中，对每个预测结果进行不确定性估计（如基于概率密度或分位数的置信度）至关重要，以降低风险。然而，现有多种动作预测方法因隐式概率表征，难以有效建模不确定性。本文提出ProbHMI，利用可逆网络将动作姿态参数化于解耦潜空间，实现概率动态建模。预测模块显式输出未来潜变量分布，从而支持有效的不确定性量化。在多个基准测试上，ProbHMI在确定性和多样性预测方面均表现优异，并验证了不确定性校准能力，这对风险感知决策至关重要。
原文摘要 · Abstract (English)
3D human motion forecasting aims to enable autonomous applications. Estimating uncertainty for each prediction (i.e., confidence based on probability density or quantile) is essential for safety-critical contexts like human-robot collaboration to minimize risks. However, existing diverse motion forecasting approaches struggle with uncertainty quantification due to implicit probabilistic representations hindering uncertainty modeling. We propose ProbHMI, which introduces invertible networks to parameterize poses in a disentangled latent space, enabling probabilistic dynamics modeling. A forecasting module then explicitly predicts future latent distributions, allowing effective uncertainty quantification. Evaluated on benchmarks, ProbHMI achieves strong performance for both deterministic and diverse prediction while validating uncertainty calibration, critical for risk-aware decision making.
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