The Animation Transformer: Visual Correspondence via Segment Matching

• URL: http://arxiv.org/abs/2109.02614v2
• Date: Wed, 8 Sep 2021 01:45:07 GMT
• Title: The Animation Transformer: Visual Correspondence via Segment Matching
• Authors: Evan Casey, V\'ictor P\'erez, Zhuoru Li, Harry Teitelman, Nick Boyajian, Tim Pulver, Mike Manh, and William Grisaitis
• Abstract summary: Animation Transformer (AnT) 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 and is publicly accessible as a creative tool in Cadmium.
• Score: 2.8387322144750726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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