ToonComposer: Streamlining Cartoon Production with Generative
  Post-Keyframing
Authors: name: Lingen Li, name: Guangzhi Wang, name: Zhaoyang Zhang, name: Yaowei Li, name: Xiaoyu Li, name: Qi Dou, name: Jinwei Gu, name: Tianfan Xue, name: Ying Shan

Traditional cartoon and anime production involves keyframing, inbetweening,
and colorization stages, which require intensive manual effort. Despite recent
advances in AI, existing methods often handle these stages separately, leading
to error accumulation and artifacts. For instance, inbetweening approaches
struggle with large motions, while colorization methods require dense per-frame
sketches. To address this, we introduce ToonComposer, a generative model that
unifies inbetweening and colorization into a single post-keyframing stage.
ToonComposer employs a sparse sketch injection mechanism to provide precise
control using keyframe sketches. Additionally, it uses a cartoon adaptation
method with the spatial low-rank adapter to tailor a modern video foundation
model to the cartoon domain while keeping its temporal prior intact. Requiring
as few as a single sketch and a colored reference frame, ToonComposer excels
with sparse inputs, while also supporting multiple sketches at any temporal
location for more precise motion control. This dual capability reduces manual
workload and improves flexibility, empowering artists in real-world scenarios.
To evaluate our model, we further created PKBench, a benchmark featuring
human-drawn sketches that simulate real-world use cases. Our evaluation
demonstrates that ToonComposer outperforms existing methods in visual quality,
motion consistency, and production efficiency, offering a superior and more
flexible solution for AI-assisted cartoon production.