ToonComposer: Streamlining Cartoon Production with Generative
  Post-Keyframing
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.
689ede65a4caabb4320e5e36
Lingen Li
claimed_verified
2025-08-18T06:57:00.008Z
66837d3c48edefb453b0640a
/avatars/b16385eaa612578728e2c6460a76b38f.svg
Lingen Li
user
l-li
689ede65a4caabb4320e5e37
Guangzhi Wang
689ede65a4caabb4320e5e38
Zhaoyang Zhang
689ede65a4caabb4320e5e39
Yaowei Li
689ede65a4caabb4320e5e3a
Xiaoyu Li
claimed_verified
2025-09-03T09:06:48.887Z
66d8284b6bddfb32e77ddafb
/avatars/edd371ecef6e7d58b945a30bbc6095ee.svg
Xiaoyu Li
user
Xiaoyu521
689ede65a4caabb4320e5e3b
Qi Dou
689ede65a4caabb4320e5e3c
Jinwei Gu
689ede65a4caabb4320e5e3d
Tianfan Xue
689ede65a4caabb4320e5e3e
Ying Shan
ToonComposer: Streamlining Cartoon Production with Generative
  Post-Keyframing
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.
ToonComposer is a generative model that unifies inbetweening and colorization in cartoon production, using sparse sketches and a cartoon adaptation method to improve visual quality and efficiency.
sparse sketch injection cartoon adaptation spatial low-rank adapter video foundation model PKBench visual quality motion consistency production efficiency