This paper introduces a new generative deep learning network for human motion
synthesis and control. Our key idea is to combine recurrent neural networks
(RNNs) and adversarial training for human motion modeling. We first describe an
efficient method for training a RNNs model from prerecorded motion data. We
implement recurrent neural networks with long short-term memory (LSTM) cells
because they are capable of handling nonlinear dynamics and long term temporal
dependencies present in human motions. Next, we train a refiner network using
an adversarial loss, similar to Generative Adversarial Networks (GANs), such
that the refined motion sequences are indistinguishable from real motion
capture data using a discriminative network. We embed contact information into
the generative deep learning model to further improve the performance of our
generative model. The resulting model is appealing to motion synthesis and
control because it is compact, contact-aware, and can generate an infinite
number of naturally looking motions with infinite lengths. Our experiments show
that motions generated by our deep learning model are always highly realistic
and comparable to high-quality motion capture data. We demonstrate the power
and effectiveness of our models by exploring a variety of applications, ranging
from random motion synthesis, online/offline motion control, and motion
filtering. We show the superiority of our generative model by comparison
against baseline models.

本研究提出了一种新的生成式深度学习网络，用于人体动作的合成和控制，通过结合循环神经网络（RNN）和对抗训练来进行优化。研究表明，该模型可以高效地生成自然流畅的动作序列，并通过各种比较与实验，证明其卓越性能和有效性。