The Animation Transformer: Visual Correspondence via Segment Matching
This addresses the need for efficient assistive tools in hand-drawn animation, offering a domain-specific solution that is incremental by building on transformer architectures.
The paper tackles the problem of visual correspondence in hand-drawn animation by learning at the segment level instead of pixel-level, proposing the Animation Transformer (AnT) to enable practical ML-assisted colorization for professional workflows.
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.