Human motion prediction from its historical poses is an essential task in computer vision; it is successfully applied for human-machine interaction and intelligent driving. Recently, significant progress has been made with variants of RNNs or LSTMs. Despite alleviating the vanishing gradient problem, the chain RNN often leads to deformities and convergence to the mean pose because of its low ability to capture long-term dependencies. To address these problems, in this paper, we propose a temporal convolutional generative adversarial network (TCGAN) to forecast high-fidelity future poses. The TCGAN uses hierarchical temporal convolution to model the long-term patterns of human motion effectively. In contrast to RNNs, the hierarchical convolution structure has recently proved to be a more efficient method for sequence-to-sequence learning in computational complexity, the number of model parameters, and parallelism. Besides, instead of traditional GANs, spectral normalization (SN) is embedded in the model to alleviate mode collapse. Compared with typical recurrent methods, the proposed model is feedforward and can produce the future poses in real-time. Extensive experiments on various human activity analysis benchmarks (i.e., H3.6M, CMU, and 3DPW MoCap) demonstrate that the model consistently outperforms the state-of-the-art methods in terms of accuracy and visualization for short-term and long-term predictions.

从其历史姿势预测人体运动是计算机视觉中的一项基本任务。它已成功应用于人机交互和智能驾驶。最近，RNN或LSTM的变体已经取得了重大进展。尽管缓解了梯度消失的问题，但是链RNN由于捕获长期依赖项的能力低，经常导致变形和收敛到平均姿势。为了解决这些问题，在本文中，我们提出了一个时间卷积生成对抗网络（TCGAN）来预测高保真的未来姿势。TCGAN使用分层时间卷积来有效地模拟人类运动的长期模式。与RNN相比，最近，在计算复杂度，模型参数数量和并行性方面，分层卷积结构已被证明是一种更有效的序列到序列学习方法。此外，代替传统的GAN，将频谱归一化（SN）嵌入模型中以减轻模式崩溃。与典型的递归方法相比，该模型具有前馈性，可以实时产生未来的姿势。在各种人类活动分析基准上进行的广泛实验（例如H3.6M，CMU和3DPW MoCap）表明，该模型在短期和长期预测的准确性和可视化方面始终优于最新技术。