Kavli Affiliate: Feng Wang
| First 5 Authors: Yuhong Zhang, Yuhong Zhang, , ,
| Summary:
Animation colorization plays a vital role in animation production, yet
existing methods struggle to achieve color accuracy and temporal consistency.
To address these challenges, we propose textbfAnimeColor, a novel
reference-based animation colorization framework leveraging Diffusion
Transformers (DiT). Our approach integrates sketch sequences into a DiT-based
video diffusion model, enabling sketch-controlled animation generation. We
introduce two key components: a High-level Color Extractor (HCE) to capture
semantic color information and a Low-level Color Guider (LCG) to extract
fine-grained color details from reference images. These components work
synergistically to guide the video diffusion process. Additionally, we employ a
multi-stage training strategy to maximize the utilization of reference image
color information. Extensive experiments demonstrate that AnimeColor
outperforms existing methods in color accuracy, sketch alignment, temporal
consistency, and visual quality. Our framework not only advances the state of
the art in animation colorization but also provides a practical solution for
industrial applications. The code will be made publicly available at
hrefhttps://github.com/IamCreateAI/AnimeColorhttps://github.com/IamCreateAI/AnimeColor.
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