论文标题
生成模型增强的人类运动预测
Generative Model-Enhanced Human Motion Prediction
论文作者
论文摘要
自然的异质性和作用的组成性使预测人类运动的任务是复杂的，因此必须稳健地对分布的分布（OOD）进行稳健性。在这里，我们根据Human36M和CMU运动捕获数据集制定了一个新的OOD基准测试，并引入了一个混合框架，以通过使用生成模型来增强它们来强化判别架构以使其变为OOD失败。当应用于当前最新的判别模型时，我们表明所提出的方法可以提高OOD的鲁棒性而无需牺牲分布性能，并且理论上可以促进模型的解释性。我们建议应考虑到OOD挑战的人类运动预测因素，并为将各种判别性体系结构限制在极端分布转移时提供了可扩展的一般框架。该代码可从https://github.com/bouracha/oodmotion获得。
The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD). Here we formulate a new OoD benchmark based on the Human3.6M and CMU motion capture datasets, and introduce a hybrid framework for hardening discriminative architectures to OoD failure by augmenting them with a generative model. When applied to current state-of-the-art discriminative models, we show that the proposed approach improves OoD robustness without sacrificing in-distribution performance, and can theoretically facilitate model interpretability. We suggest human motion predictors ought to be constructed with OoD challenges in mind, and provide an extensible general framework for hardening diverse discriminative architectures to extreme distributional shift. The code is available at https://github.com/bouracha/OoDMotion.
