Visual correspondence is a fundamental building block on the way to building assistive tools for hand-drawn animation. However, while a large body of work has focused on learning visual correspondences at the pixel-level, few approaches have emerged to learn correspondence at the level of line enclosures (segments) that naturally occur in hand-drawn animation. Exploiting this structure in animation has numerous benefits: it avoids the intractable memory complexity of attending to individual pixels in high resolution images and enables the use of real-world animation datasets that contain correspondence information at the level of per-segment colors. To that end, we propose the Animation Transformer (AnT) which uses a transformer-based architecture to learn the spatial and visual relationships between segments across a sequence of images. AnT enables practical, state-of-art AI-assisted colorization for professional animation workflows and is publicly accessible as a creative tool in Cadmium.

翻译：视觉通信是建立手绘动画辅助工具的一条基本基石。 然而,虽然大量工作的重点是学习像素级的视觉通信,但很少出现方法来学习自然在手工绘制动画中出现的线条附文(部分)一级的通信。 将这一结构用于动画有许多好处:它避免了在高分辨率图像中关注单个像素的难以解决的记忆复杂性,并使得能够使用真实世界的动画数据集,该数据集包含着每分层颜色一级的对应信息。 为此,我们提议了动画变异器(AnT),该变异器使用变异器结构来学习一系列图像之间的空间和视觉关系。 AnT为专业动画工作流程提供了实用的、最先进的、最先进的人工的色彩化工具,并且作为Cadmium的创造性工具向公众开放。