Generative Cutout Animation

Ivan Puhachov\(^1\), Noam Aigerman\(^1,2\), Thibault Groueix\(^3\), Mikhail Bessmeltsev\(^1\)

\(^1\) Université de Montréal
\(^2\) Mila
\(^3\) Adobe

Eurographics 2026

---

Abstract

Cutout animation is one of the earliest forms of animation, and to this day remains a popular technique featured in numerous films including Monty Python and South Park series. Most computer animation systems, however, focus on different styles, including cel animation, making cutout animation somewhat underexplored. As creating cutouts is meticulous, we propose a novel generative cutout animation system. Taking a skeletal animation and a text prompt as input, we automatically generate a 2.5D cutout rig ready for production in films and games. Our system optimizes cutout images with an SDS (Score Distillation Sampling) loss with a LoRA (Low-Rank Adaptation) prior, in multiple target poses. Na"ively optimizing an SDS loss, however, would lead to inconsistent target pose images, and, as a result, blurry or transparent cutouts. To address this, we introduce a novel optimization with techniques targeting pose and noise consistency, resulting in coherent target images and sharp cutouts. We validate our system by demonstrating a gallery of results, comparing with previous works, ablations, and other analyses. Once generated, our cutout rigs can be used both for the given input animation and repurposed for other animations or edited as independent assets.