  Hand-drawn cartoon animation employs sketches and flat-color segments to
create the illusion of motion. While recent advancements like CLIP, SVD, and
Sora show impressive results in understanding and generating natural video by
scaling large models with extensive datasets, they are not as effective for
cartoons. Through our empirical experiments, we argue that this ineffectiveness
stems from a notable bias in hand-drawn cartoons that diverges from the
distribution of natural videos. Can we harness the success of the scaling
paradigm to benefit cartoon research? Unfortunately, until now, there has not
been a sizable cartoon dataset available for exploration. In this research, we
propose the Sakuga-42M Dataset, the first large-scale cartoon animation
dataset. Sakuga-42M comprises 42 million keyframes covering various artistic
styles, regions, and years, with comprehensive semantic annotations including
video-text description pairs, anime tags, content taxonomies, etc. We pioneer
the benefits of such a large-scale cartoon dataset on comprehension and
generation tasks by finetuning contemporary foundation models like Video CLIP,
Video Mamba, and SVD, achieving outstanding performance on cartoon-related
tasks. Our motivation is to introduce large-scaling to cartoon research and
foster generalization and robustness in future cartoon applications. Dataset,
Code, and Pretrained Models will be publicly available.
