Due to the remarkable progress of deep generative models, animating images
has become increasingly efficient, whereas associated results have become
increasingly realistic. Current animation-approaches commonly exploit structure
representation extracted from driving videos. Such structure representation is
instrumental in transferring motion from driving videos to still images.
However, such approaches fail in case the source image and driving video
encompass large appearance variation. Moreover, the extraction of structure
information requires additional modules that endow the animation-model with
increased complexity. Deviating from such models, we here introduce the Latent
Image Animator (LIA), a self-supervised autoencoder that evades need for
structure representation. LIA is streamlined to animate images by linear
navigation in the latent space. Specifically, motion in generated video is
constructed by linear displacement of codes in the latent space. Towards this,
we learn a set of orthogonal motion directions simultaneously, and use their
linear combination, in order to represent any displacement in the latent space.
Extensive quantitative and qualitative analysis suggests that our model
systematically and significantly outperforms state-of-art methods on VoxCeleb,
Taichi and TED-talk datasets w.r.t. generated quality.

介绍了一种名为 LIA 的基于自我监督的自编码器模型，该模型可以在潜在空间中进行线性导航以运动图像。LIA 避免了使用结构表示，通过同时学习正交运动方向和其线性组合来生成视频，结果在 VoxCeleb、Taichi 和 TED-talk 数据集上的质量明显优于现有方法。