To achieve seamless collaboration between robots and humans in a shared environment, accurately predicting future human movements is essential. Human motion prediction has traditionally been approached as a sequence prediction problem, leveraging historical human motion data to estimate future poses. Beginning with vanilla recurrent networks, the research community has investigated a variety of methods for learning human motion dynamics, encompassing graph-based and generative approaches. Despite these efforts, achieving accurate long-term predictions continues to be a significant challenge. In this regard, we present the Adversarial Motion Transformer (AdvMT), a novel model that integrates a transformer-based motion encoder and a temporal continuity discriminator. This combination effectively captures spatial and temporal dependencies simultaneously within frames. With adversarial training, our method effectively reduces the unwanted artifacts in predictions, thereby ensuring the learning of more realistic and fluid human motions. The evaluation results indicate that AdvMT greatly enhances the accuracy of long-term predictions while also delivering robust short-term predictions

翻译：摘要：为了实现机器人与人类在共享环境中的无缝协作，准确预测人类未来运动至关重要。人体运动预测传统上被视为一个序列预测问题，利用历史人体运动数据来估计未来姿态。从最初的简单循环网络开始，研究界已探索了多种学习人体运动动力学的方法，包括基于图的方法和生成式方法。尽管付出了诸多努力，实现准确的长期预测仍是一项重大挑战。为此，我们提出了对抗性运动Transformer（Adversarial Motion Transformer, AdvMT），这是一种融合了基于Transformer的运动编码器与时间连续性判别器的新型模型。这种组合能够有效地同时捕捉帧内的空间与时间依赖性。通过对抗训练，我们的方法能够有效减少预测中的不良伪影，从而确保学习到更真实、更流畅的人体运动。评估结果表明，AdvMT在显著提升长期预测精度的同时，也提供了稳健的短期预测能力。