论文标题
对人类运动综合的对抗性关注
Adversarial Attention for Human Motion Synthesis
论文作者
论文摘要
分析人类动作是许多学科的核心主题,从人类计算机互动到娱乐,虚拟现实和医疗保健。深度学习在实时捕获人姿势方面取得了令人印象深刻的结果。另一方面,由于受试者间的可变性高,人类运动分析模型通常由于在医疗保健等领域提供的专业数据非常有限而无法概括到看不见受试者的数据。但是,获取人类运动数据集非常耗时,具有挑战性且昂贵。因此,人类运动合成是深度学习和计算机视觉中的关键研究问题。我们通过应用端到端训练的基于注意力的概率深层对抗模型,提出了一种可控制人类运动综合的新方法。我们表明,通过使用对抗性注意,我们可以在短期和长期视野上产生合成的人类运动。此外,我们表明,我们可以通过补充合成动作来补充现有数据集,在没有足够的真实数据的情况下改善深度学习模型的分类性能。
Analysing human motions is a core topic of interest for many disciplines, from Human-Computer Interaction, to entertainment, Virtual Reality and healthcare. Deep learning has achieved impressive results in capturing human pose in real-time. On the other hand, due to high inter-subject variability, human motion analysis models often suffer from not being able to generalise to data from unseen subjects due to very limited specialised datasets available in fields such as healthcare. However, acquiring human motion datasets is highly time-consuming, challenging, and expensive. Hence, human motion synthesis is a crucial research problem within deep learning and computer vision. We present a novel method for controllable human motion synthesis by applying attention-based probabilistic deep adversarial models with end-to-end training. We show that we can generate synthetic human motion over both short- and long-time horizons through the use of adversarial attention. Furthermore, we show that we can improve the classification performance of deep learning models in cases where there is inadequate real data, by supplementing existing datasets with synthetic motions.