Kavli Affiliate: Feng Wang
| First 5 Authors: Yuxue Yang, Lue Fan, Zuzeng Lin, Feng Wang, Zhaoxiang Zhang
| Summary:
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.
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