Real-world objects perform complex motions that involve multiple independent
motion components. For example, while talking, a person continuously changes
their expressions, head, and body pose. In this work, we propose a novel method
to decompose motion in videos by using a pretrained image GAN model. We
discover disentangled motion subspaces in the latent space of widely used
style-based GAN models that are semantically meaningful and control a single
explainable motion component. The proposed method uses only a few $(\approx10)$
ground truth video sequences to obtain such subspaces. We extensively evaluate
the disentanglement properties of motion subspaces on face and car datasets,
quantitatively and qualitatively. Further, we present results for multiple
downstream tasks such as motion editing, and selective motion transfer, e.g.
transferring only facial expressions without training for it.

本文提出了一种使用预训练图像 GAN 模型分解视频中动作的新方法，通过发现神经网络的深层潜在空间中语义上有意义的因子，实现运动解缠。作者对面部和汽车数据集进行了广泛的评估，并展示了对于运动编辑和选择性运动转移（仅转移面部表情）等多种下游任务的结果。