LongAnimation: Long Animation Generation with Dynamic Global-Local Memory
Nan Chen, Mengqi Huang, Yihao Meng, Zhendong Mao

TL;DR
LongAnimation introduces a dynamic global-local memory framework for long animation colorization, effectively maintaining color consistency over extended video sequences by integrating global features with local details.
Contribution
The paper proposes a novel dynamic global-local paradigm and a framework that enhances long-term color consistency in animation colorization, addressing limitations of previous local-only methods.
Findings
Effective long-term color consistency in animation achieved
Outperforms existing short-term methods on long video sequences
Demonstrates robustness across open-domain animation data
Abstract
Animation colorization is a crucial part of real animation industry production. Long animation colorization has high labor costs. Therefore, automated long animation colorization based on the video generation model has significant research value. Existing studies are limited to short-term colorization. These studies adopt a local paradigm, fusing overlapping features to achieve smooth transitions between local segments. However, the local paradigm neglects global information, failing to maintain long-term color consistency. In this study, we argue that ideal long-term color consistency can be achieved through a dynamic global-local paradigm, i.e., dynamically extracting global color-consistent features relevant to the current generation. Specifically, we propose LongAnimation, a novel framework, which mainly includes a SketchDiT, a Dynamic Global-Local Memory (DGLM), and a Color…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Human Motion and Animation · Computer Graphics and Visualization Techniques
