  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 ML-assisted colorization for professional
animation workflows and is publicly accessible as a creative tool in Cadmium.
