LayerAnimate: Layer-level Control for Animation

TL;DR

LayerAnimate introduces a layer-aware video diffusion framework that enables fine-grained layer-level control in animation, addressing data scarcity with a novel curation pipeline and outperforming existing methods in quality and usability.

Contribution

We propose a novel layer-aware diffusion framework for animation that incorporates a data curation pipeline to overcome data scarcity and enhances control and quality.

Findings

Outperforms current methods in animation quality

Provides precise layer-level control

Demonstrates usability for professionals and amateurs

Abstract

Traditional animation production decomposes visual elements into discrete layers to enable independent processing for sketching, refining, coloring, and in-betweening. Existing anime generation video methods typically treat animation as a distinct data domain different from real-world videos, lacking fine-grained control at the layer level. To bridge this gap, we introduce LayerAnimate, a novel video diffusion framework with layer-aware architecture that empowers the manipulation of layers through layer-level controls. The development of a layer-aware framework faces a significant data scarcity challenge due to the commercial sensitivity of professional animation assets. To address the limitation, we propose a data curation pipeline featuring Automated Element Segmentation and Motion-based Hierarchical Merging. Through quantitative and qualitative comparisons, and user study, we demonstrate that LayerAnimate outperforms current methods in terms of animation quality, control precision, and usability, making it an effective tool for both professional animators and amateur enthusiasts. This framework opens up new possibilities for layer-level animation applications and creative flexibility. Our code is available at https://layeranimate.github.io.