Report
The Animation Transformer: Visual Correspondence via Segment Matching
| العنوان: | The Animation Transformer: Visual Correspondence via Segment Matching |
|---|---|
| المؤلفون: | Casey, Evan, Pérez, Víctor, Li, Zhuoru, Teitelman, Harry, Boyajian, Nick, Pulver, Tim, Manh, Mike, Grisaitis, William |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Graphics |
| الوصف: | 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. Comment: ICCV 2021 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2109.02614 |
| رقم الانضمام: | edsarx.2109.02614 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |