We present a new implicit warping framework for image animation using sets of source images through the transfer of the motion of a driving video. A single cross- modal attention layer is used to find correspondences between the source images and the driving image, choose the most appropriate features from different source images, and warp the selected features. This is in contrast to the existing methods that use explicit flow-based warping, which is designed for animation using a single source and does not extend well to multiple sources. The pick-and-choose capability of our framework helps it achieve state-of-the-art results on multiple datasets for image animation using both single and multiple source images. The project website is available at https://deepimagination.cc/implicit warping/
翻译:我们为图像动画提供了一个新的隐含扭曲框架,通过传输驱动录象的动动,使用一组源图像。 使用一个单一的跨模式关注层来查找源图像和驱动图像之间的对应关系,从不同的源图像中选择最合适的特征,并扭曲选定的特征。 这与使用明确的流动扭曲的现有方法形成对照,即使用单一源为动画设计,但并不很好地扩展到多个来源。 我们框架的选选选能力有助于它利用单个和多个源图像在多个图像动画数据集中取得最新的最新结果。 项目网站可在 https://deepimagination.cc/ imperclicit warping/ https://deepimagination.cc/ acc/ acclicic warping/ 上查阅。