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Computer Science > Computer Vision and Pattern Recognition

arXiv:2405.07425 (cs)
[Submitted on 13 May 2024]

Title:Sakuga-42M Dataset: Scaling Up Cartoon Research

Authors:Zhenglin Pan, Yu Zhu, Yuxuan Mu
View a PDF of the paper titled Sakuga-42M Dataset: Scaling Up Cartoon
Research, by Zhenglin Pan and 2 other authors
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    Abstract: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.

Comments: Arxiv Pre-print. Work in Progress
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2405.07425 [cs.CV]
          (or arXiv:2405.07425v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2405.07425
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Zhenglin Pan [view email]
[v1] Mon, 13 May 2024 01:50:05 UTC (9,048 KB)
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