MagicAnime: A Hierarchically Annotated, Multimodal and Multitasking Dataset with Benchmarks for Cartoon Animation Generation

Shuolin Xu; Bingyuan Wang; Zeyu Cai; Fangteng Fu; Yue Ma; Tongyi Lee; Hongchuan Yu; Zeyu Wang

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arXiv · 2507.20368

MagicAnime: A Hierarchically Annotated, Multimodal and Multitasking Dataset with Benchmarks for Cartoon Animation Generation

Shuolin Xu·Bingyuan Wang·Zeyu Cai·Fangteng Fu·Yue Ma·Tongyi Lee·Hongchuan Yu·Zeyu Wang

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Abstract

Generating high-quality cartoon animations multimodal control is challenging due to the complexity of non-human characters, stylistically diverse motions and fine-grained emotions. There is a huge domain gap between real-world videos and cartoon animation, as cartoon animation is usually abstract and has exaggerated motion. Meanwhile, public multimodal cartoon data are extremely scarce due to the difficulty of large-scale automatic annotation processes compared with real-life scenarios. To bridge this gap, We propose the MagicAnime dataset, a large-scale, hierarchically annotated, and multimodal dataset designed to support multiple video generation tasks, along with the benchmarks it includes. Containing 400k video clips for image-to-video generation, 50k pairs of video clips and keypoints for whole-body annotation, 12k pairs of video clips for video-to-video face animation, and 2.9k pairs of video and audio clips for audio-driven face animation. Meanwhile, we also build a set of multi-modal cartoon animation benchmarks, called MagicAnime-Bench, to support the comparisons of different methods in the tasks above. Comprehensive experiments on four tasks, including video-driven face animation, audio-driven face animation, image-to-video animation, and pose-driven character animation, validate its effectiveness in supporting high-fidelity, fine-grained, and controllable generation.

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BibTeX RIS

Shuolin Xu, Bingyuan Wang, Zeyu Cai, Fangteng Fu, Yue Ma, Tongyi Lee, Hongchuan Yu, Zeyu Wang. 2025-07-27. MagicAnime: A Hierarchically Annotated, Multimodal and Multitasking Dataset with Benchmarks for Cartoon Animation Generation. https://arxiv.org/abs/2507.20368

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